U.S. patent application number 17/452220 was filed with the patent office on 2022-02-10 for automated learning of anomalies in media streams with external feed labels.
The applicant listed for this patent is AT&T Intellectual Property I, L.P.. Invention is credited to Lee Begeja, Raghuraman Gopalan, Zhu Liu, Bernard S. Renger, Behzad Shahraray, Eric Zavesky.
Application Number | 20220043838 17/452220 |
Document ID | / |
Family ID | |
Filed Date | 2022-02-10 |
United States Patent
Application |
20220043838 |
Kind Code |
A1 |
Zavesky; Eric ; et
al. |
February 10, 2022 |
AUTOMATED LEARNING OF ANOMALIES IN MEDIA STREAMS WITH EXTERNAL FEED
LABELS
Abstract
Methods, computer-readable media, and devices are disclosed for
providing a notification of an anomaly in a media content that is
associated with an event type. For example, a method may include a
processing system including at least one processor for detecting a
first anomaly from a first media content, generating a first
anomaly signature for the first anomaly, obtaining a notification
of a first event, the notification including an event type, time
information, and location information of the first event,
correlating the first anomaly to the notification of the first
event, and labeling the first anomaly signature with the event
type. The processing system may further detect a second anomaly
from a second media content that matches the first anomaly
signature and transmit a notification of a second event of the
event type when it is detected that the second anomaly matches the
first anomaly signature.
Inventors: |
Zavesky; Eric; (Austin,
TX) ; Begeja; Lee; (Gillette, NJ) ; Gopalan;
Raghuraman; (Dublin, CA) ; Renger; Bernard S.;
(New Providence, NJ) ; Shahraray; Behzad;
(Holmdel, NJ) ; Liu; Zhu; (Marlboro, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AT&T Intellectual Property I, L.P. |
Atlanta |
GA |
US |
|
|
Appl. No.: |
17/452220 |
Filed: |
October 25, 2021 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
15984052 |
May 18, 2018 |
11157524 |
|
|
17452220 |
|
|
|
|
International
Class: |
G06F 16/28 20060101
G06F016/28; G06N 3/08 20060101 G06N003/08; G06F 16/2457 20060101
G06F016/2457 |
Claims
1. A method comprising: detecting, by a processing system including
at least one processor, a first anomaly from a first media content;
generating, by the processing system, a first anomaly signature for
the first anomaly; obtaining, by the processing system, a
notification of a first event, wherein the notification comprises
an event type of the first event, time information of the first
event, and location information of the first event; correlating, by
the processing system, the first anomaly to the notification of the
first event; labeling, by the processing system, the first anomaly
signature with the event type; detecting, by the processing system,
a second anomaly from a second media content that matches the first
anomaly signature; and transmitting, by the processing system, a
notification of a second event of the event type when it is
detected that the second anomaly matches the first anomaly
signature.
2. The method of claim 1, wherein the first media content includes
at least one of: images; video; or audio.
3. The method of claim 1, wherein the detecting the first anomaly
comprises detecting a plurality of anomalies having a threshold
similarity from a plurality of media contents, wherein the
plurality of anomalies includes the first anomaly, wherein the
plurality of media contents includes the first media content, and
wherein the first anomaly signature is for the plurality of
anomalies.
4. The method of claim 3, wherein the first anomaly signature is
based upon a plurality of features from the plurality of
anomalies.
5. The method of claim 4, wherein the plurality of features
includes at least one of: visual features; or audio features.
6. The method of claim 3, wherein the detecting the plurality of
anomalies having the threshold similarity comprises applying a
machine learning algorithm based upon a plurality of features from
the plurality of media contents.
7. The method of claim 6, wherein the machine learning algorithm
comprises at least one of: a deep neural network; a generative
adversarial network; an exponential smoothing algorithm; or a
reinforcement learning algorithm.
8. The method of claim 1, wherein the first media content comprises
metadata including: location information of the first media
content; and time information of the first anomaly.
9. The method of claim 8, further comprising: detecting a third
anomaly in the first media content that is later in time than the
first anomaly and earlier in time than the time information of the
first event; generating a second anomaly signature for the third
anomaly; correlating the third anomaly to the notification of the
first event; and identifying the first anomaly as a cause of the
third anomaly, when the first anomaly and the third anomaly are
both correlated to the notification of the first event.
10. The method of claim 9, further comprising: labeling the second
anomaly signature with the event type; and labeling the first
anomaly signature as a potential cause signature of the event
type.
11. The method of claim 1, wherein the notification of the second
event comprises: the event type; location information of the second
media content; and time information of the second anomaly.
12. The method of claim 11, further comprising: activating at least
one sensor at a location associated with the location information
of the second media content, when it is detected that the second
anomaly matches the first anomaly signature, wherein the
notification of the second event further comprises sensor data from
the at least one sensor.
13. The method of claim 11, further comprising: receiving a
feedback for the notification of the second event comprising one
of: a positive feedback; or a negative feedback; and updating the
first anomaly signature based upon features of the second
anomaly.
14. The method of claim 13, wherein the features of the second
anomaly comprise a positive training example for the first anomaly
signature when the feedback is the positive feedback, and wherein
the features of the second anomaly comprise a negative training
example for the first anomaly signature when the feedback is the
negative feedback.
15. The method of claim 1, wherein the notification of the second
event is sent to at least one of: a monitoring device; or an
automated signaling device at a location associated with the
location information of the second media content.
16. The method of claim 1, wherein the notification of the first
event comprises: a short message service-based alert; a really
simple syndication-based alert; an email-based alert; a radio
broadcast alert; or a television broadcast alert.
17. The method of claim 1, wherein the notification of the first
event is obtained from: a weather alert service; a traffic alert
service; a public safety alert service; or an aggregator alert
service.
18. A non-transitory computer-readable medium storing instructions
which, when executed by a processing system including at least one
processor, cause the processing system to perform operations, the
operations comprising: detecting a first anomaly from a first media
content; generating a first anomaly signature for the first
anomaly; obtaining a notification of a first event, wherein the
notification comprises an event type of the first event, time
information of the first event, and location information of the
first event; correlating the first anomaly to the notification of
the first event; labeling the first anomaly signature with the
event type; detecting a second anomaly from a second media content
that matches the first anomaly signature; and transmitting a
notification of a second event of the event type when it is
detected that the second anomaly matches the first anomaly
signature.
19. A device comprising: a processing system including at least one
processor; and a computer-readable medium storing instructions
which, when executed by the processing system, cause the processing
system to perform operations, the operations comprising: detecting
a first anomaly from a first media content; generating a first
anomaly signature for the first anomaly; obtaining a notification
of a first event, wherein the notification comprises an event type
of the first event, time information of the first event, and
location information of the first event; correlating the first
anomaly to the notification of the first event; labeling the first
anomaly signature with the event type; detecting a second anomaly
from a second media content that matches the first anomaly
signature; and transmitting a notification of a second event of the
event type when it is detected that the second anomaly matches the
first anomaly signature.
20. The device of claim 19, wherein the detecting the first anomaly
comprises detecting a plurality of anomalies having a threshold
similarity from a plurality of media contents, wherein the
plurality of anomalies includes the first anomaly, wherein the
plurality of media contents includes the first media content, and
wherein the first anomaly signature is for the plurality of
anomalies.
Description
[0001] This application is a continuation of U.S. patent
application Ser. No. 15/984,052, filed on May 18, 2018, now U.S.
Pat. No. 11,157,524, which is herein incorporated by reference in
its entirety.
[0002] The present disclosure relates generally to emergency alert
networks, and more particularly to devices, computer-readable
media, and methods for providing a notification of an anomaly in a
media content that is associated with an event type.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] The teaching of the present disclosure can be readily
understood by considering the following detailed description in
conjunction with the accompanying drawings, in which:
[0004] FIG. 1 illustrates an example system related to the present
disclosure;
[0005] FIG. 2 illustrates a flowchart of an example method for
providing a notification of an anomaly in a media content that is
associated with an event type, in accordance with the present
disclosure; and
[0006] FIG. 3 illustrates an example high-level block diagram of a
computing device specifically programmed to perform the steps,
functions, blocks, and/or operations described herein.
[0007] To facilitate understanding, identical reference numerals
have been used, where possible, to designate identical elements
that are common to the figures.
DETAILED DESCRIPTION
[0008] In one example, the present disclosure discloses a method,
computer-readable medium, and device for providing a notification
of an anomaly in a media content that is associated with an event
type. For example, a method may include a processing system
including at least one processor detecting a first anomaly from a
first media content, generating a first anomaly signature for the
first anomaly, obtaining a notification of a first event, the
notification including an event type, time information, and
location information of the first event, correlating the first
anomaly to the notification of the first event, and labeling the
first anomaly signature with the event type. The processing system
may further detect a second anomaly from a second media content
that matches the first anomaly signature and transmit a
notification of a second event of the event type when it is
detected that the second anomaly matches the first anomaly
signature.
[0009] Examples of the present disclosure provide a system for
automated identification of anomalies in media content and labeling
of anomalies as new semantic events or insights (e.g., a traffic
jam, people entering a store, etc.). In particular, examples of the
present disclosure utilize digitized reports, e.g., a Short Message
Service (SMS)/text message-based alert, a Really Simple Syndication
(RSS) feed-based alert, an email-based alert, a radio broadcast
alert, a television broadcast alert, and so forth. The report may
be disseminated by a weather alert service (e.g., the National
Weather Service (NWS), a state or local weather alert service, or
an independent weather news source), a traffic alert service (e.g.,
from a state department of transportation (DOT) or independent
traffic news source), a public safety alert service (e.g., from a
local governmental authority such as a fire department, a police
department, an emergency medical services (EMS) department, a
hazardous materials department (hazmat), etc.), an aggregator alert
service, and so on. Examples of the present disclosure may be
applied to macro-level problems (e.g., flooding, evacuation needs,
etc.) as well as micro-level problems, such as a retail store being
under-staffed. In particular, events of interest can be observed as
anomalies in media sources and identified as particular events
through correlation with external data feeds. Future events of a
same event type may then be predicted through machine learning
models trained in accordance with the present disclosure.
[0010] In one example, anomalies are detected by unsupervised
historical linkage and observation, and then labeled with external
semantic tags derived from external data sources. It should be
noted that examples of the present disclosure relate to various
types of media content including video, still images, and audio. To
illustrate, a camera may be directed at a roadway and capture video
of an accident. In addition, the accident may be identified as an
anomaly in the video (e.g., two cars unusually close together
and/or stopped, where vehicles are usually in motion). The anomaly,
e.g., "unusual" features in the video stream, may be determined via
a comparison of features from one or more frames in a given time
window versus "normal" or average features from a larger time
period. The features may include low-level invariant image data,
such as colors (e.g., RGB (red-green-blue) or CYM
(cyan-yellow-magenta) raw data (luminance values) from a
CCD/photo-sensor array), shapes, color moments, color histograms,
edge distribution histograms, etc. Visual features may also relate
to movement in a video and may include changes within images and
between images in a sequence (e.g., video frames or a sequence of
still image shots), such as color histogram differences or a change
in color distribution, edge change ratios, standard deviation of
pixel intensities, contrast, average brightness, and the like. In
one example, the system may perform image salience detection
processes, e.g., applying an image salience model and then
performing an image recognition algorithm over the "salient"
portion of the image(s). Thus, in one example, visual features may
also include a recognized object, a length to width ratio of an
object, a velocity of an object estimated from a sequence of images
(e.g., video frames), and so forth.
[0011] Features used to determine if a given portion of the video
stream include an anomaly may also include low-level audio features
such as: spectral centroid, spectral roll-off, signal energy,
mel-frequency cepstrum coefficients (MFCCs), linear predictor
coefficients (LPC), line spectral frequency (LSF) coefficients,
loudness coefficients, sharpness of loudness coefficients, spread
of loudness coefficients, octave band signal intensities, and so
forth. Additional audio features may also include high-level
features, such as: words and phrases. For instance, one example may
utilize speech recognition pre-processing to obtain an audio
transcript and to rely upon various keywords or phrases as data
points.
[0012] In the present example, there may also be a report of the
accident that is contained in a traffic report feed that gives a
time and a location, and this may then be correlated with the
anomaly determined to be present in the video feed. For instance,
the traffic report feed may include the text: "accident on I-98
mile marker 82 reported at 11:15 am." The feed thus includes a
location, "I-98 mile marker 82," and a time, "11:15 am." The report
may also include the date of the accident, e.g., Jan. 1, 2018 and
so on. The system may comprise a database or may access a database
that may be used to determine that the location in the traffic
report feed is relevant to the location of the camera. The
relevance of the location may be determined in any number of ways.
For instance, the location may be relevant if it is determined to
be within a threshold distance from the location of the camera, if
it is within a same town or zip code, if it is within a given
geofence surrounding the location of the camera, if it pertains to
a same roadway or segment of a roadway as the camera, if it
pertains to a same building and/or a same room as the camera, and
so forth.
[0013] The system may then review the media content for a
designated time prior to and including the time provided in the
report (e.g., from 30 minutes prior to 11:15 am until 11:15 am,
from 15 minutes prior to 11:15 am until 11:15 am, etc.) to
determine if there is an anomaly in the media content. When there
is an anomaly present in the media content, the features may be
stored as an "anomaly signature." In one example, the traffic
report description of the incident then becomes the label, or event
type, for the anomaly signature; in this case, "accident." In one
example, an anomaly signature may be created that represents
multiple anomalies having a threshold similarity. For instance, the
anomaly signature may comprise a machine learning model (MLM) that
is trained based upon the plurality of features from the plurality
of anomalies. For instance, each of the similar anomalies may
comprise a positive example that is applied to a machine learning
algorithm (MLA) to generate the anomaly signature (e.g., a MLM). In
one example, the anomalies used to train the MLM may be determined
to be "similar" when the anomalies are associated with the same or
similar events from one or more external data feeds. For instance,
when a plurality of anomalies are all determined to be associated
with "accidents" based upon one or more traffic report data feeds,
the features of the plurality of anomalies may then be used to
create an aggregate anomaly signature for the sematic concept of
"accident." In another example, the MLM may comprise the average
features representing a cluster of the plurality of similar
anomalies in a feature space.
[0014] The machine learning algorithm (MLA), or machine learning
model (MLM) trained via the MLA may comprise, for example, a deep
learning neural network, or deep neural network (DNN), a generative
adversarial network (GAN), a support vector machine (SVM), e.g., a
binary, non-binary, or multi-class classifier, a linear or
non-linear classifier, and so forth. In one example, the MLA may
incorporate an exponential smoothing algorithm (such as double
exponential smoothing, triple exponential smoothing, e.g.,
Holt-Winters smoothing, and so forth), reinforcement learning
(e.g., using positive and negative examples after deployment as a
MLM), and so forth. It should be noted that various other types of
MLAs and/or MLMs may be implemented in examples of the present
disclosure, such as k-means clustering and/or k-nearest neighbor
(KNN) predictive models, support vector machine (SVM)-based
classifiers, e.g., a binary classifier and/or a linear binary
classifier, a multi-class classifier, a kernel-based SVM, etc., a
distance-based classifier, e.g., a Euclidean distance-based
classifier, or the like, and so on. In one example, the anomaly
signature may include those features which are determined to be the
most distinguishing features of the anomaly, e.g., those features
which are quantitatively the most different from what is considered
statistically normal or average from a source of the media content,
e.g., the top 20 features, the top 50 features, etc.
[0015] In one example, the anomaly signature (e.g., a MLM) may be
deployed as a network filter to process media content from the same
and/or additional media sources to identify patterns in the
features of the media content(s) that match the anomaly signature.
In one example, a match may be determined using any of the visual
features and/or audio features mentioned above. For instance, a
match may be determined when there is threshold measure of
similarity among the features of the media source(s) and the
anomaly signature. In one example, the media source(s) may be
analyzed using a time-based sliding window, extracting features,
and comparing the features to the anomaly signature. Thus, the next
time there is a similar sequence of events, e.g., similar imagery
and/or audio, it may be associated with the earlier event and may
be identified as a potential other event of the same event
type.
[0016] In one example, a notification of a potential new event of
the same event type may be provided to one or more appropriate
recipients. For instance, for a potential road accident, police,
EMS, DOT, and other governmental or private entities may be
automatically provided within notification of the potential new
event. The notification may include the event type (e.g.,
"accident"), a time, and a location. For instance, a camera
capturing the new event may have a location provided in metadata of
the video stream or may have a known location that is stored in a
database accessible to the system. Similarly, the video stream may
include time stamp information for frames of the video. Thus, the
system may determine the relevant time of the event. In one
example, the notification may further include a percentage
prediction error or confidence score. For instance, a confidence
score may be proportional to the quantitative similarity between
the detected anomaly and the anomaly signature.
[0017] In one example, the notification may also include a portion
of the media content, e.g., a short clip of video, a series of one
or more still images, or the like. Accordingly, the one or more
recipients of the notification may inspect the portion of the media
content to determine if the potential new event has been accurately
detected and notified. In one example, notifications can be used to
manage multiple media sources by providing a monitoring station
with feeds from media sources with anomalies currently matching
anomaly signatures, and suppressing feeds from other available
media sources. In one example, the one or more recipients may
provide a response indicating whether the notification was
accurate. The response may be used to further refine the anomaly
signature. For instance, the features of the second anomaly may be
used as a positive training example for the anomaly signature when
the feedback is a positive feedback. Conversely, the features of
the second anomaly may be used as a negative training example for
the anomaly signature when the feedback is a negative feedback. In
another example, if there is an anomaly detected that matches an
anomaly signature, but it is later found that there is no
corresponding event for the anomaly via a secondary source (data
feed), it may be considered a false positive. For instance, this
aspect may be utilized where no affirmative feedback regarding a
notification is provided to the system by a recipient. In one
example, the notification may also include an automated message
presented visually on a sign of a building, roadway or the like,
and/or presented in an audio format, e.g., a recording played via
one or more speakers deployed in an environment, via one or more
mobile phone speakers, etc. In one example, the notification may
also include changing a signal in an environment (e.g., a traffic
signal), closing or opening an automated door, window, or other
barrier, and so forth.
[0018] In one example, an event may also be correlated to possible
additional visual anomalies that may be associated with the same
event. For instance, a visual anomaly of an accident may be
correlated to a report of the accident from a traffic report data
feed. However, if media content from the media source is inspected
further back in time, e.g., 5 minutes prior, 2 minutes prior, etc.,
there may also be another anomaly (e.g., a premeditating event that
may have caused the accident) that may be detected. For instance,
there may be a visual anomaly of an obstruction or debris (e.g., a
large package or object, a flat tire, and so on) on a roadway in a
video stream in addition to a visual anomaly of the subsequent
accident several minutes later. In such case, by extending a search
window further back in time from the time of the event indicated in
the traffic report data feed, the earlier anomaly may also be found
and correlated to the event. In addition, the two anomalies may
also be correlated/associated with each other. For instance,
without having specific knowledge of causation, the fact that the
two anomalies appeared in the same media content close in time to
each other, and at or close to the time of the event contained in
the traffic report data feed, the earlier anomaly may be considered
as a causal event for the later event that is actually
reported.
[0019] In one example, an additional anomaly signature may be
generated for the earlier visual anomaly that is considered to be
the causal event. In addition, the additional anomaly signature may
be deployed as a network filter to process media content from the
same and/or additional media sources to identify patterns in the
features of the media content(s) that match the additional anomaly
signature. In one example, the additional anomaly signature may be
labeled with the same event type as the later visual anomaly along
with metadata indicating that the additional anomaly signature is a
possible causal event related to a subsequent event of the
indicated event type. Thus, the label may be "accident" since that
is what is reported in the traffic/news feed, rather than a label
of "obstruction on road," but it will still be useful to provide
relevant information to a decision maker, e.g., next time when an
errand tire is detected on the road, it can be properly detected as
a known anomaly and then promptly reported, thereby potentially
averting a subsequent accident as a result. For example, a
notification in the roadway via a display can flash a warning such
as "Caution!--debris detected ahead in the roadway" and so on. In
addition, although an obstruction on a road is not always a prior
condition for an accident, in instances where there is such a
correlation, it is something that may be indicated by a visual
anomaly that may be detected, learned, and notified in accordance
with the present disclosure. Examples of the present disclosure may
relate to various types of events that may be detected as anomalies
in media sources, such as a car crash, a flood portion of a road or
property, a power outage, etc., which can all be correlated to
various external data feeds relating to traffic, weather, law
enforcement, public safety, and so forth.
[0020] As just one additional example, a camera may be trained on a
wall of a basement and detect an anomaly of basement flooding. In
addition, a later building maintenance report may include a
basement flooding remediation work order noting a time. The
location may also be noted in the work order, or implied if the
system is only deployed with respect to a single building. The
report of "basement flooding" may then be correlated to the anomaly
that is detected in a video feed or series of still images from the
camera. In addition, an anomaly signature for "basement flooding"
may be created and labeled, and then used to detect and notify of a
subsequent flooding event that has now been learned.
[0021] It should be noted that examples of the present disclosure
may incorporate media content from multiple sources to increase the
accuracy of the detection and classification of anomalies. For
instance, multiple cameras directed at a same location from
different angles may provide media content that can be used to
detect a same anomaly/same event. In addition, the anomalies
detected from the multiple media sources may be used to verify the
accuracy of detection of an anomaly from the other media sources.
In addition, in one example, the present disclosure may provide
recommended remedies based on the difference between anomalies and
regular events in similar contextual conditions. For instance,
multiple video feeds from nearby cameras may include a forest of
conifer trees failing during bad weather while showing one or
several ginkgo trees surviving. The system may thus suggest
planting more ginkgo trees in the affected areas. Examples of the
present disclosure therefore improve public safety and efficiency
for automated insights through correlated media content and
semantic data content. In addition, examples of the present
disclosure provide semantic labeling of anomalies based on locally
observed conditions. For instance, some areas may be flooded, but
an anomaly detected in one region may be "normal" and therefore
dismissible in another region. Examples of the present disclosure
may also be used to increase accuracy of predictions from
traditional sources (e.g., for weather, traffic, expected crowd
capacity, etc.) using combined media content and external data feed
information, to promote faster repairs (e.g., locating the cause of
anomaly), and so forth. These and other aspects of the present
disclosure are discussed in greater detail below in connection with
the examples of FIGS. 1-3.
[0022] To aid in understanding the present disclosure, FIG. 1
illustrates a block diagram depicting one example of an environment
100 suitable for performing or enabling the steps, functions,
operations, and/or features described herein. As illustrated in
FIG. 1, the environment 100 includes a telecommunication service
provider network 110. In one example, telecommunication service
provider network 110 may comprise a core network, a backbone
network or transport network, such as an Internet Protocol
(IP)/multi-protocol label switching (MPLS) network, where label
switched routes (LSRs) can be assigned for routing Transmission
Control Protocol (TCP)/IP packets, User Datagram Protocol (UDP)/IP
packets, and other types of protocol data units (PDUs), and so
forth. It should be noted that an IP network is broadly defined as
a network that uses Internet Protocol to exchange data packets.
However, it will be appreciated that the present disclosure is
equally applicable to other types of data units and transport
protocols, such as Frame Relay, and Asynchronous Transfer Mode
(ATM). In one example, the telecommunication service provider
network 110 uses a network function virtualization infrastructure
(NFVI), e.g., host devices or servers that are available as host
devices to host virtual machines comprising virtual network
functions (VNFs). In other words, at least a portion of the
telecommunication service provider network 110 may incorporate
software-defined network (SDN) components.
[0023] The telecommunication service provider network 110 may be in
communication with one or more access networks. For instance,
wireless access network 115 may comprise a cellular network (e.g.,
a Universal Mobile Telecommunications System (UMTS) terrestrial
radio access network (UTRAN), an evolved UTRAN (eUTRAN), a base
station subsystem (BSS), e.g., a Global System for Mobile
communication (GSM) radio access network (GRAN), a 2G, 3G, 4G
and/or 5G network, a Long Term Evolution (LTE) network, and the
like). In such examples, telecommunication service provider network
110 may include evolved packet core (EPC) network components,
network switching subsystem (NSS)/GSM core network and/or General
Packet Radio Service (GPRS) core network components, and so forth.
Thus, in one example, wireless access network 115 may include at
least one cell tower 120, which may alternatively comprise a
cellular base station, such as a base transceiver station (BTS), a
NodeB, an evolved NodeB (eNodeB), and the like, a non-cellular
wireless access point, and so forth. Cell tower 120 may include
antenna arrays 121 (e.g., remote radio heads (RRHs)), a mast 122,
and other components (not shown). The telecommunication service
provider network 110 and the wireless access network 115 may be
operated by different service providers, or by a same service
provider.
[0024] In one example, telecommunication service provider network
110 is connected to other networks 118. In one example, other
networks 118 may represent one or more enterprise networks, a
circuit switched network (e.g., a public switched telephone network
(PSTN)), a cable network, a digital subscriber line (DSL) network,
a metropolitan area network (MAN), an Internet service provider
(ISP) network, and the like. In one example, the other networks 118
may include different types of networks. In another example, the
other networks 118 may be the same type of network. In one example,
the other networks 118 may represent the Internet in general.
[0025] In one example, telecommunication service provider network
110 is also connected to access networks 114. The access networks
114 may include a wireless access network (e.g., an IEEE
802.11/Wi-Fi network and the like), a Wide Area Network (WAN), a
cellular access network, such as an evolved Universal Terrestrial
Radio Access Network (eUTRAN) that includes one or more eNodeBs, a
PSTN access network, a cable access network, a digital subscriber
line (DSL) network, a metropolitan area network (MAN), other types
of wired access networks, an Internet service provider (ISP)
network, and the like. Alternatively, or in addition, access
networks 114 may represent corporate, governmental or educational
institution LANs, a home/residential LAN, and the like. In one
embodiment, the access networks 114 may all be different types of
access networks, may all be the same type of access network, or
some access networks may be the same type of access network and
other may be different types of access networks. The other networks
118, the access networks 114, wireless access network 115, and the
telecommunication service provider network 110 may be operated by
different service providers, the same service provider, or a
combination thereof. The other networks 118, the access networks
114, wireless access network 115, and the telecommunication service
provider network 110 may be interconnected via one or more
intermediary networks (not shown) which may utilize various
different protocols and technologies for transporting
communications in the form of data packets, datagrams, protocol
data units (PDUs), and the like, such as one or more IP/MPLS
networks, one or more frame relay networks, one or more ATM
networks, and so forth.
[0026] The example of FIG. 1 further includes several media sources
180-182 which may include cameras 191-193 (e.g., video cameras,
cameras to capture sequences of still images, etc.) and microphones
194-196. The media sources 180-182 may generate streams of media
content comprising still images, audio, and/or video. Further
illustrated in FIG. 1 is an event detection station 150 which may
comprise all or a portion of a computing device or system, such as
computing system 300, and/or processing system 302 as described in
connection with FIG. 3 below, and may be configured to perform
various steps, functions, and/or operations in connection with
examples of the present disclosure for providing a notification of
an anomaly in a media content that is associated with an event
type. It should be noted that the terms "configure" and
"reconfigure" may refer to programming or loading a processing
system with computer-readable/computer-executable instructions,
code, and/or programs, e.g., in a distributed or non-distributed
memory, which when executed by a processor, or processors, of the
processing system within a same device or within distributed
devices, may cause the processing system to perform various
functions. Such terms may also encompass providing variables, data
values, tables, objects, or other data structures or the like which
may cause a processing system executing computer-readable
instructions, code, and/or programs to function differently
depending upon the values of the variables or other data structures
that are provided. As referred to herein a "processing system" may
comprise a computing device including one or more processors, or
cores (e.g., as illustrated in FIG. 3 and discussed below) or
multiple computing devices collectively configured to perform
various steps, functions, and/or operations in accordance with the
present disclosure.
[0027] In one example, the event detection station 150 may collect
and process media content from one or more of the media sources
180-182. In the present example, monitoring station 150 may be
connected to media source 180, or may obtain media content from
media sources 181 and/or media source 182 via one or more of the
access networks 114. In particular, media sources 180-182 may
provide media content that may be used by event detection station
150 to perform operations for providing a notification of an
anomaly in a media content that is associated with an event type,
in accordance with the present disclosure. An example method for
providing a notification of an anomaly in a media content that is
associated with an event type is illustrated in FIG. 2 and
discussed in greater detail below.
[0028] In one example, event detection station 150 may obtain event
notifications from one or more information feed sources 132, such
as a weather alert service, a traffic alert service, a public
safety alert service, an aggregator alert service, and so on. The
event notifications may be in formats such as a SMS/text
message-based alert, a RSS feed-based alert, an email-based alert,
and so forth. The event notifications obtained by event detection
station 150 may all be in a same format or may be in a plurality of
different formats. In one example, the event detection station 150
may be subscribed to a feed service from one or more of the
information feed sources 132. The event notifications from
information feed sources 132 may be transmitted point-to-point, or
may be broadcast or multicast. Although the example of FIG. 1
illustrates information feed sources 132 as connected to other
networks 118, it should be noted that the information feed sources
132 may alternatively or additionally disseminate a frequency
modulation (FM) or amplitude modulation (AM) radio broadcast alert,
a television broadcast alert, a wireless emergency alert (WEA), and
so forth.
[0029] As illustrated in FIG. 1, the media source 180 (e.g., camera
191 and microphone 194) may be directed at a roadway 145 and may
capture a video or a sequence of images which may be relayed to
event detection station 150. In the present example, the video
stream may include imagery of a box 148 (e.g., debris) on the
roadway 145. In addition, the video stream may include imagery of a
crash between cars 140 and 142. Either or both of these incidents
may be identified as anomalies in the video stream by event
detection station 150. In addition, anomaly signatures may be
created for either or both of these anomalies and labeled in
accordance with an external data feed from one of information feed
sources 132. For example, a text alert from one of information feed
sources 132 may state: "accident on I-95 mile marker 82 reported at
11:15 am." In one example, the anomaly signature for the box 148 in
the road 145 may be tagged as a possible causal event for the
detected event of "accident."
[0030] In addition, in one example, either or both anomaly
signatures may be deployed as filters by event detection station
150 to process future media content (e.g., video stream(s)) from
media source 180, other media sources directed at roadway 145,
and/or other media sources directed at other roadways. In
particular, when a portion of a video stream is a match to one of
the filters (i.e., to one of the anomaly signatures), a possible
event may be notified to one or more monitoring devices 134 without
awaiting a possible confirmatory notification from one of the
information feed sources 132. In the present example, the
notification may be sent in the form of an SMS/text message, an
email, an RSS feed, etc. via access networks 114, telecommunication
service provider network 110, other networks 118, and so forth. In
one example, the notification may comprise an instruction to change
a signal in an environment (e.g., a traffic signal 152 and/or
traffic signal 154). For instance, the traffic signals 152 and 154
may be changed to blinking yellow lights to signal to other
motorists that there may be an accident in the vicinity and to use
caution.
[0031] In another example, event detection station 150 may gather
images, video, and/or audio from media source 181 (e.g., camera 192
and microphone 195). For instance, media source 181 may be directed
at building 125 (e.g., a home or a business), which may have an
address of "123 Center Street." In one example, the media content
may include the sound of breaking glass, imagery of an open or
broken window 127, an open door 129, etc. In any case, the media
content may be determined to include an anomaly when the media
content differs from the normal or expected media content from the
media source 181 relating to house 125. In addition, in one
example, the event detection station 150 may obtain event
notifications from one of the information feed sources 132, e.g., a
police report data feed that may include a message: "vandalism at
123 Center Street reported 2:30 pm." In such an example, the event
detection station 150 may determine that the location in the event
report matches the location of media source 181. In addition, the
event detection station 150 may determine that the time of the
event notification matches a time for which an anomaly in the media
content from media source 181 was detected. In such case, an
anomaly signature may be created from the media content (i.e., from
the time of the anomaly) and labeled as "vandalism."
[0032] The event detection station 150 may then deploy a filter
comprising the anomaly signature to process additional media
content from media source 181 and/or from other media sources. For
instance, other media sources that may be deployed and directed at
other buildings may feed media content to the event detection
station 150 to determine if and when any patterns in such media
content match the anomaly signature, and may therefore indicate a
possible event of the event type "vandalism." In such case, a
possible act of vandalism may be reported to one or more of
monitoring devices 134 (e.g., a terminal at a police station, a
terminal for a neighborhood security officer, etc.). Notably, such
a notification may be obtained before any actual person may observe
and report such an act of vandalism. Alternatively, or in addition,
the event detection station 150 may present a notification in an
audio format, e.g., a recording played via one or more speakers
deployed in an environment, via one or more mobile phone speakers,
etc. For instance, the event detection station 150 may cause a
warning to be played out via a loudspeaker at or near building 125
to notify neighbors, to deter a possible perpetrator from
additional acts, etc. In one example, the detection of a match to
an anomaly signature may cause event detection station 150 to
activate one or more additional devices, such as turning on an
additional camera facing the building 125 from a different vantage
point, turning on an additional camera that is outward facing from
building 125, reorienting a camera, closing an automated door or a
gate on the property of building 125, turning on a spotlight, and
so forth.
[0033] In still another example event detection station 150 may
gather images, video, and/or audio from media source 182 (e.g.,
camera 193 and microphone 196). For instance, media source 182 may
be directed at a cell tower 120, which may include antenna arrays
121, a mast 122, and other components (not shown). In one example,
the media content from media source 182 may include imagery of one
of antenna arrays 121 being missing, one of antenna arrays 121
being out of a normal position, etc. For instance, over a
significant time period, the event detection station 150 may obtain
a series of images of cell tower 120 from media source 182. The
images may all include the antenna arrays 121 in a particular
configuration and having set positions. The event detection station
may determine that there is an anomaly based upon one or more
images that then show one or more of the antenna arrays 121 being
out of a previous position.
[0034] In addition, in one example, the event detection station 150
may obtain event notifications from one of the information feed
sources 132, e.g., a network repair/trouble ticket system. For
instance, there may be a subsequent repair order that indicates:
"antenna orientation problem, cell tower 120, April 28." In such an
example, the event detection station 150 may determine that the
location in the event report (e.g., cell tower 120) matches the
location of media source 182 (cell tower 120). In addition, the
event detection station 150 may determine that the time of the
event notification matches a time for which an anomaly in the media
content from media source 182 is detected (e.g., during the same
day, during a two day window, etc.). In such case, an anomaly
signature may be created from the media content (i.e., from the
time of the anomaly) and labeled as "antenna orientation
problem."
[0035] The event detection station 150 may then deploy a filter
comprising the anomaly signature to process additional media
content from media source 182 and/or from other media sources. For
instance, other media sources that may be deployed and directed at
other cell towers may feed media content to event detection station
150. In addition, event detection station 150 may determine if and
when any patterns in such media content match the anomaly
signature, and may therefore indicate a possible event of the event
type "antenna orientation problem." In such case, a possible
antenna orientation problem may be reported to one or more of
monitoring devices 134 (e.g., a network technician or supervisor,
network operations personnel, etc.). Notably, such a notification
may be obtained before any actual person may observe and report
such an antenna orientation problem.
[0036] In one example, the detection of media content matching the
anomaly signature may also cause event detection station 150 to
activate and collect data from one or more sensors 156. For
instance, sensors 156 may include a wind speed sensor which may
provide useful data that the possible antenna orientation problem
may involve high winds. In such an example, a notification to one
of monitoring devices 134 may include corresponding wind speed
measurements. Alternatively, or in addition, sensors 156 may
collect measurements continuously, but may only report the
measurements in response to a request from event detection station
150. For instance, sensors 156 may collect and store 24 hours of
measurements. Thus, when event detection station 150 determines
that there is media content that matches an anomaly signature, the
event detection station 150 may then request the sensor
measurements from sensors 156 for the same time period during which
the anomaly is detected.
[0037] It should be noted that the system 100 has been simplified.
In other words, the system 100 may be implemented in a different
form than that illustrated in FIG. 1. For example, the system 100
may be expanded to include additional networks, and additional
network elements (not shown) such as wireless transceivers and/or
base stations, border elements, routers, switches, policy servers,
security devices, gateways, a network operations center (NOC), a
content distribution network (CDN) and the like, without altering
the scope of the present disclosure. In addition, system 100 may be
altered to omit various elements, substitute elements for devices
that perform the same or similar functions and/or combine elements
that are illustrated as separate devices.
[0038] As just one example, the operations described above with
respect to event detection station 150 may alternatively or
additionally be performed by a device, or a plurality of devices in
telecommunication service provider network 110, access network 114,
other networks 118, and so forth, such as servers 112. In one
example, a first device may process media content to identify
anomalies, a second device may correlate the anomalies to events
identified in one or more external data feeds, a third device may
create and update anomaly signatures based upon feedback, a fourth
device may implement anomaly signatures as filters for real-time
media content feeds, a fifth device may control the activation of
sensors after detection of an anomaly, and so forth. In addition,
although media sources 180-182 are illustrated in a wire-based
networking deployment, in other, further, and different examples,
any one or more of media sources 180-182 may alternatively or
additionally be equipped for wireless communication. For example,
media source 182 may capture media content relating to cell tower
120 in addition to transmitting the media content to other
network-based devices via the same cell tower 120. Thus, these and
other modifications are all contemplated within the scope of the
present disclosure.
[0039] FIG. 2 illustrates a flowchart of an example method 200 for
providing a notification of an anomaly in a media content that is
associated with an event type. In one example, steps, functions
and/or operations of the method 200 may be performed by event
detection station 150, and/or server 112 of FIG. 1, or any one or
more of such devices in conjunction with one another and/or with
other components, such as one or more media sources 180-182,
sensors 156, information feed sources 132, monitoring devices 134,
and so forth. In one example, the steps, functions, or operations
of method 200 may be performed by a computing device or processing
system 300, and/or processor 302 as described in connection with
FIG. 3 below. Similarly, in one example, the steps, functions, or
operations of method 200 may be performed by a processing system
comprising one or more computing devices collectively configured to
perform various steps, functions, and/or operations of the method
200. For instance, multiple instances of the computing device or
processing system 300 may collectively function as a processing
system, e.g., comprising a control system, and/or control system in
conjunction with other components. For illustrative purposes, the
method 200 is described in greater detail below in connection with
an example performed by a processor, such as processor 302. The
method begins in step 205 and may proceed to optional step 210 or
to step 215.
[0040] At step 210, the processor detects a first anomaly from a
first media content. The first media content may comprise images,
video, e.g., video sequences/clips, and/or audio, e.g., audio
sequences/clips. In one example, the first media content comprises
metadata including location information of the first media content
and time information of the first anomaly. In one example, the
detecting the first anomaly comprises detecting a plurality of
anomalies having a threshold similarity from a plurality of media
contents. For example, the plurality of anomalies may include the
first anomaly, the plurality of media contents may include the
first media content, and the first anomaly signature may be for the
plurality of anomalies. For instance, the detecting of the
plurality of anomalies having the threshold similarity may comprise
applying a machine learning algorithm (MLA), such as a clustering
algorithm, based upon a plurality of features from the plurality of
media contents. The plurality of media contents may be from a same
media source or a plurality of different media sources. In one
example, the machine learning algorithm may comprise at least one
of: a deep neural network (DNN), a generative adversarial network
(GAN), or the like. In one example, the machine learning algorithm
may further include an exponential smoothing algorithm, (e.g.,
Holt-Winters triple exponential smoothing) and/or a reinforcement
learning algorithm. It should be noted that various other types of
MLAs and/or MLMs may be implemented in examples of the present
disclosure, such as k-means clustering and/or k-nearest neighbor
(KNN) predictive models, support vector machine (SVM)-based
classifiers, e.g., a binary classifier and/or a linear binary
classifier, a multi-class classifier, a kernel-based SVM, etc., a
distance-based classifier, e.g., a Euclidean distance-based
classifier, or the like, and so on.
[0041] The plurality of features may include visual features, audio
features, or both video and audio features. Visual features may
include low-level invariant image data, changes with images and
between images in a sequence (e.g., video frames or a sequence of
still image shots), such as color histogram differences or a change
in color distribution, a recognized object, a length to width ratio
of an object, a velocity of an object estimated from a sequence of
images (e.g., video frames), and so forth. Audio features may
include low-level features such as: spectral centroid, spectral
roll-off, signal energy, and so forth. Audio features may also
include high level features, such as identified words and phrases.
For instance, one example may utilize speech recognition
pre-processing to obtain an audio transcript and to rely upon
various keywords or phrases as data points. High-level audio
features may also include identified noises of a particular source,
e.g., a certain animal call, a plane, a helicopter, an automobile,
etc.
[0042] It should also be noted that although the terms, "first,"
"second," "third," etc., are used herein, the use of these terms
are intended as labels only. Thus, the use of a term such as
"third" in one example does not necessarily imply that the example
must in every case include a "first" and/or a "second" of a similar
item. In other words, the use of the terms "first," "second,"
"third," and "fourth," do not imply a particular number of those
items corresponding to those numerical values. In addition, the use
of the term "third" for example, does not imply a specific sequence
or temporal relationship with respect to a "first" and/or a
"second" of a particular type of item, unless otherwise
indicated.
[0043] At step 215, the processor generates a first anomaly
signature for the first anomaly. In one example, the first anomaly
signature may include those features which are determined to be the
most distinguishing features of the anomaly, e.g., those features
which are quantitatively the most different from what is considered
statistically normal or average from a source of the media content,
e.g., the top 20 features, the top 50 features, etc. In one
example, the first anomaly signature is based upon a plurality of
features from a plurality of anomalies (e.g., a plurality of
anomalies that are clustered based upon feature similarities
according to a machine learning algorithm).
[0044] At step 220, the processor obtains a notification of a first
event. In one example, the notification comprises an event type of
the first event, time information of the first event, and location
information of the first event. The notification may be in a format
such as a SMS/text message-based alert, a RSS feed-based alert, an
email-based alert, a FM or an AM radio broadcast alert, a
television broadcast alert, a wireless emergency alert (WEA), and
so forth. The notification of the first event may be obtained from
an external source, such as a weather alert service, a traffic
alert service, a public safety alert service, and so forth, or an
aggregator alert service providing multiple types of alerts.
[0045] At step 225, the processor correlates the first anomaly to
the notification of the first event. For instance, the correlating
may comprise determining a relevance of the notification of the
first event to the first anomaly based upon a correspondence of a
time and a location indicated in the notification of the first
event with a time of the first anomaly and a location of the source
of the media content. For instance, the location indicated for the
notification may be relevant if it is determined to be within a
threshold distance from the location of the media source, if it
indicates a same mile marker or segment of a highway as the media
source, if it indicates a same address, a same street, a same
building, a same campus, etc. as the media source, and so forth. In
one example, the processor accesses a database that may be used to
determine the location of the media source. In one example, the
time relevance may be determined in accordance with a lookback
period for which the processor may inspect the media content for
any detected anomalies. For instance the lookback period may
comprise ten minutes prior to the time indicated in the
notification, 15 minutes prior to the time indicated in the
notification, 30 minutes prior to the time indicated in the
notification, and so forth.
[0046] At step 230, the processor labels the first anomaly
signature with the event type. For instance, the label may be
stored as metadata along with the first anomaly signature.
[0047] At optional step 235, the processor may detect a third
anomaly in the first media content that is later in time than the
first anomaly and earlier in time than the time information of the
first event that is indicated in the notification. In one example,
optional step 235 may comprise similar operations to that which is
described above in connection with step 210.
[0048] At optional step 240, the processor may generate a second
anomaly signature for the third anomaly. In one example, optional
step 240 may comprise similar operations to that which is described
above in connection with step 215.
[0049] At optional step 245, the processor may correlate the third
anomaly to the notification of the first event. In one example,
optional step 245 may comprise similar operations to that which is
described above in connection with step 220.
[0050] At optional step 250, the processor may identify the first
anomaly as a cause of the third anomaly, when the first anomaly and
the third anomaly are both correlated to the notification of the
first event.
[0051] At optional step 255, the processor may label the second
anomaly signature with the event type. For instance, the label may
be stored as metadata along with the second anomaly signature.
[0052] At optional step 260, the processor may label the first
anomaly signature as a potential cause signature of the event. For
instance, if the event type is "car accident," it may be considered
that the third anomaly (and the second anomaly signature) relate to
the actual car accident captured in the media content, whereas the
first anomaly may be an obstruction on the road, such as a box
(e.g., debris) in the road, an animal on the road, etc. At optional
step 260, the exact nature of the first anomaly may be unknown to
the processor. However, the temporal relation of the first anomaly
and the third anomaly may be considered as indicative of a possible
causal relationship that may be recorded in a label added at
optional step 260. Thus, the first anomaly signature can be deemed
to be a potential cause signature of the event.
[0053] At step 265, the processor detects a second anomaly from a
second media content that matches the first anomaly signature. For
instance, the first anomaly signature may be deployed as a filter
to process additional media content from the same or a different
media source as the first media content. The filter may be deployed
at a same device as the processor (e.g., the filter may comprise a
process loaded in a memory and executed by the processor) and/or at
one or more additional devices in a network. In an example where
the filter is at a different device, the processor may receive a
notification from the device operating the filter when there is a
detection of the second anomaly.
[0054] At optional step 270, the processor may activate at least
one sensor at a location associated with the location information
of the second media content, when it is detected that the second
anomaly matches the first anomaly signature. In addition, the
processor may receive sensor data from the at least one sensor
following the activation of the at least one sensor and the
collection of the sensor data.
[0055] At step 275, the processor transmits a notification of a
second event of the event type when it is detected that the second
anomaly matches the first anomaly signature. In one example, the
processor includes the event type, location information of the
second media content, and time information of the second anomaly in
the notification of the second event. In one example, the processor
may further include a portion of the second media content
containing the second anomaly in the notification of the second
event, or a link (e.g., a URL) providing access to a stored copy of
the portion of the second media content containing the second
anomaly. In addition, in an example where sensor data is collected
by at least one sensor activated at optional step 270, the
notification of the second event may further comprise sensor data
from the at least one sensor. For example, if an anomaly indicates
a problem with one or more antennas of a cell tower, a wind speed
sensor may collect useful data indicating high winds that may be
included in the notification. For instance, a recipient of the
notification may find it more credible that there is an actual
problem when the notification indicates an event type of "problem
with antenna orientation" and the wind speed sensor measurement
indicates high winds, i.e., confirmation of a potential cause of
the detected anomaly.
[0056] In one example, the notification of the second event may be
transmitted to a monitoring device (e.g., a device of police, fire,
EMS, or DOT personnel, a device of a homeowner, a building manager,
security personnel, and so forth). In one example, the notification
may be alternatively or additionally transmitted to an automated
signaling device at a location of the media source of the second
media content. For instance, the notification may comprise
instructions or may otherwise cause the automated signaling device
to display an alert. For instance, a roadway sign may change from a
green light to a blinking yellow or red light to signal caution to
motorists. In another example, the roadway sign may display text
such as "possible accident ahead--use caution." In still another
example, a speed limit may be reduced in an area near the incident.
The notification of the first event may be transmitted in one or
more formats such as a SMS-based alert, a RSS-based alert, an
email-based alert, a radio broadcast alert, a television broadcast
alert, and so forth.
[0057] At optional step 280, the processor may receive a feedback
for the notification of the second event. The feedback may be a
positive feedback or a negative feedback. For instance, a recipient
of the notification of the second event may review a portion of the
second media content and may conclude that the event label is
accurate or that the event label is inaccurate, and provide
feedback to the processor according to the conclusion reached. In
another example, a person may be dispatched to the location of the
media source providing the second media content, and may provide
feedback regarding the accuracy of the event label. In still
another example, there may be an additional notification of the
second event that is obtained from an external source, such as a
weather alert service, a traffic alert service, a public safety
alert service, and so forth, or an aggregator alert service
providing multiple types of alerts. For instance, there may be
confirmation of an accident in a police report shortly following
the notification of the second event transmitted at step 275 and
possibly before a recipient is able to review the notification.
[0058] At optional step 285, the processor may update the first
anomaly signature based upon features of the second anomaly. For
example, the features of the second anomaly may comprise a positive
training example for the first anomaly signature when the feedback
is a positive feedback, and may comprise a negative training
example for the first anomaly signature when the feedback is a
negative feedback.
[0059] Following step 275, or any of the optional steps 280-285,
the method 200 may proceed to step 295. At step 295, the method 200
ends.
[0060] It should be noted that the method 200 may be expanded to
include additional steps or may be modified to include additional
operations with respect to the steps outlined above. For example,
the method 200 may be expanded to include repeating the steps
210-230 or steps 210-260 through multiple iterations, repeating
steps 265-275 or steps 265-280 through multiple iterations, and so
on. In another example, the method 200 may be expanded to include
learning regular actions in response to an event of a particular
event type, and then including a suggested course of action in the
notification of a second event of the event type (e.g., suggesting
the "typical" response that is learned over time). In another
example, the method may be expanded to include automatically
implementing one or more actions that are learned as a response
pattern. For instance, if a recipient often activates a second
camera in the vicinity of a camera from which the second media
content is captured, the next time an event of the event type is
detected, the processor may automatically capture media content
from a nearby camera, may reorient a nearby camera, and so forth.
In addition, the processor may also provide video from the nearby
camera, e.g., in addition to the second media content. Thus, these
and other modifications are all contemplated within the scope of
the present disclosure.
[0061] In addition, it should be noted that although not
specifically specified, one or more steps, functions or operations
of the method 200 may include a storing, displaying and/or
outputting step as required for a particular application. In other
words, any data, records, fields, and/or intermediate results
discussed in the respective methods can be stored, displayed and/or
outputted to another device as required for a particular
application. Furthermore, steps or blocks in FIG. 2 that recite a
determining operation or involve a decision do not necessarily
require that both branches of the determining operation be
practiced. In other words, one of the branches of the determining
operation can be deemed as an optional step. In addition, one or
more steps, blocks, functions, or operations of the above described
method 200 may comprise optional steps, or can be combined,
separated, and/or performed in a different order from that
described above, without departing from the example embodiments of
the present disclosure.
[0062] Furthermore, the capturing and dissemination of any of the
captured video and/or audio are only performed in full compliance
with the pertinent privacy rules and policies that are in effect at
the time. In other words, the captured video and/or audio of any
individuals would only be done with the permission of the
individuals (e.g., opting-into a service with full notice of the
potential actions of capturing and dissemination of video and/or
audio) or as permitted by law.
[0063] FIG. 3 depicts a high-level block diagram of a computing
device or processing system specifically programmed to perform the
functions described herein. For example, any one or more components
or devices illustrated in FIG. 1 or described in connection with
the method 200 may be implemented as the processing system 300. As
depicted in FIG. 3, the processing system 300 comprises one or more
hardware processor elements 302 (e.g., a microprocessor, a central
processing unit (CPU) and the like), a memory 304, (e.g., random
access memory (RAM), read only memory (ROM), a disk drive, an
optical drive, a magnetic drive, and/or a Universal Serial Bus
(USB) drive), a module 305 for providing a notification of an
anomaly in a media content that is associated with an event type,
and various input/output devices 306, e.g., a camera, a video
camera, storage devices, including but not limited to, a tape
drive, a floppy drive, a hard disk drive or a compact disk drive, a
receiver, a transmitter, a speaker, a display, a speech
synthesizer, an output port, and a user input device (such as a
keyboard, a keypad, a mouse, and the like).
[0064] Although only one processor element is shown, it should be
noted that the computing device may employ a plurality of processor
elements. Furthermore, although only one computing device is shown
in the Figure, if the method(s) as discussed above is implemented
in a distributed or parallel manner for a particular illustrative
example, i.e., the steps of the above method(s) or the entire
method(s) are implemented across multiple or parallel computing
devices, e.g., a processing system, then the computing device of
this Figure is intended to represent each of those multiple
computing devices. For example, when the present method(s) are
implemented in a distributed or parallel manner, any one or more
steps of the present method(s) can be implemented by any one or
more of the multiple or parallel computing devices of the
processing system. Furthermore, one or more hardware processors can
be utilized in supporting a virtualized or shared computing
environment. The virtualized computing environment may support one
or more virtual machines representing computers, servers, or other
computing devices. In such virtualized virtual machines, hardware
components such as hardware processors and computer-readable
storage devices may be virtualized or logically represented. The
hardware processor 302 can also be configured or programmed to
cause other devices to perform one or more operations as discussed
above. In other words, the hardware processor 302 may serve the
function of a central controller directing other devices to perform
the one or more operations as discussed above.
[0065] It should be noted that the present disclosure can be
implemented in software and/or in a combination of software and
hardware, e.g., using application specific integrated circuits
(ASIC), a programmable logic array (PLA), including a
field-programmable gate array (FPGA), or a state machine deployed
on a hardware device, a computing device, or any other hardware
equivalents, e.g., computer readable instructions pertaining to the
method(s) discussed above can be used to configure a hardware
processor to perform the steps, functions and/or operations of the
above disclosed method(s). In one example, instructions and data
for the present module or process 305 for providing a notification
of an anomaly in a media content that is associated with an event
type (e.g., a software program comprising computer-executable
instructions) can be loaded into memory 304 and executed by
hardware processor element 302 to implement the steps, functions or
operations as discussed above in connection with the example method
200. Furthermore, when a hardware processor executes instructions
to perform "operations," this could include the hardware processor
performing the operations directly and/or facilitating, directing,
or cooperating with another hardware device or component (e.g., a
co-processor and the like) to perform the operations.
[0066] The processor executing the computer readable or software
instructions relating to the above described method(s) can be
perceived as a programmed processor or a specialized processor. As
such, the present module 305 for providing a notification of an
anomaly in a media content that is associated with an event type
(including associated data structures) of the present disclosure
can be stored on a tangible or physical (broadly non-transitory)
computer-readable storage device or medium, e.g., volatile memory,
non-volatile memory, ROM memory, RAM memory, magnetic or optical
drive, device or diskette and the like. Furthermore, a "tangible"
computer-readable storage device or medium comprises a physical
device, a hardware device, or a device that is discernible by the
touch. More specifically, the computer-readable storage device may
comprise any physical devices that provide the ability to store
information such as data and/or instructions to be accessed by a
processor or a computing device such as a computer or an
application server.
[0067] While various embodiments have been described above, it
should be understood that they have been presented by way of
example only, and not limitation. Thus, the breadth and scope of a
preferred embodiment should not be limited by any of the
above-described example embodiments, but should be defined only in
accordance with the following claims and their equivalents.
* * * * *