Method and system for cluster-based video monitoring and event categorization

• US 9,213,903 B1
• Filed: 10/08/2014
• Issued: 12/15/2015
• Est. Priority Date: 07/07/2014
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Laska, Jason N., Nelson, Gregory R., Duffy, Greg, Mitsuji, Hiro, Hill, Cameron, Davidsson, Martin, Montalbo, Michael D., Wan, Tung Yuen
• Primary Examiner(s): Werner, David N
• Application Number:
  US14/510,007
• Time in Patent Office: 433 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 18/24147 Distances to closest patterns, e.g. nearest neighbour classification
• CPC Class Codes: G06F 18/243 relating to the number of classes
• CPC Class Codes: G06F 18/2431 Multiple classes
• CPC Class Codes: G06F 18/285 Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/0488 using a touch-screen or digitiser, e.g. input of commands through traced gestures
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06V 10/235 based on user input or interaction
• CPC Class Codes: G06V 10/95 structured as a network, e.g. client-server architectures
• CPC Class Codes: G06V 20/40 in video content extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/44 Event detection
• CPC Class Codes: G06V 20/46 Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 40/172 Classification, e.g. identification
• CPC Class Codes: G08B 13/19608 Tracking movement of a target, e.g. by detecting an object predefined as a target, using target direction and or velocity to predict its new position
• CPC Class Codes: H04N 21/2181 comprising remotely distributed storage units, e.g. when movies are replicated over a plurality of video servers distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS], H04L67/1097
• CPC Class Codes: H04N 21/239 : Interfacing the upstream path of the transmission network, e.g. prioritizing client content requests hybrid fiber coaxial [HFC] networks for upstream channel allocation for video distribution H04L12/2801; flow control in data networks H04L47/10; real-time communication protocols in data switching networks H04L65/00; scheduling or organising the servicing of application requests H04L67/60
• CPC Class Codes: H04N 21/44231 : Monitoring of peripheral device or external card, e.g. to detect processing problems in a handheld device or the failure of an external recording device configuring of peripheral devices in general G06F9/4411; reporting information sensed by appliance or service execution status of appliance services in a home automation network H04L12/2823; monitoring the status of connected device in data switched networks H04L43/0817
• CPC Class Codes: H04N 23/632 : for displaying or modifying preview images prior to image capturing, e.g. variety of image resolutions or capturing parameters
• CPC Class Codes: H04N 23/633 : for displaying additional information relating to control or operation of the camera
• CPC Class Codes: H04N 23/66 : Remote control of cameras or camera parts, e.g. by remote control devices
• CPC Class Codes: H04N 23/667 : Camera operation mode switching, e.g. between still and video, sport and normal or high- and low-resolution modes
• CPC Class Codes: H04N 23/69 : Control of means for changing angle of the field of view, e.g. optical zoom objectives or electronic zooming
• CPC Class Codes: H04N 5/144 : Movement detection for video coding H04N19/503; analysis of motion in general G06T7/20
• CPC Class Codes: H04N 5/145 : Movement estimation for video coding H04N19/51
• CPC Class Codes: H04N 5/2628 : Alteration of picture size, shape, position or orientation, e.g. zooming, rotation, rolling, perspective, translation
• CPC Class Codes: H04N 7/18 : Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
• CPC Class Codes: H04N 7/181 : for receiving images from a plurality of remote sources

Abstract

A computing system obtains a respective motion vector for each of a series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream. In response to receiving the respective motion vector for each of the series of motion event candidates, the computing system determines a spatial relationship between the respective motion vector of said each motion event candidate to one or more existing clusters established based on a plurality of previously processed motion vectors, and in accordance with a determination that the respective motion vector of a first motion event candidate of the series of motion event candidates falls within a respective range of at least a first existing cluster of the one or more existing clusters, assigns the first motion event candidate to at least a first event category associated with the first existing cluster.

Claims

1. A method of categorizing a motion event candidate, comprising:

obtaining a respective motion vector for each of a series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream, comprising;

identifying at least one object in motion in a respective video segment associated with the motion event candidate;

determining a respective motion track of the at least one object in motion within a predetermined duration; and

generating the respective motion vector for the motion event candidate based on the determined respective motion track of the at least one object in motion;

in response to receiving the respective motion vector for each of the series of motion event candidates, determining a spatial relationship, in a vector event space, between the respective motion vector of said each motion event candidate to one or more existing clusters established based on a plurality of previously processed motion vectors; and

in accordance with a determination that the respective motion vector of a first motion event candidate of the series of motion event candidates falls within a respective spatial range of at least a first existing cluster of the one or more existing clusters, assigning the first motion event candidate to at least a first event category associated with the first existing cluster.

2. The method of claim 1, wherein the first event category is a category for unrecognized events.

3. The method of claim 1, wherein the first event category is a category for recognized events.

4. The method of claim 1, further comprising:

in accordance with a determination that the respective motion vector of a second motion event candidate of the series of motion event candidates falls beyond a respective range of any existing cluster;

assigning the second motion event candidate to a category for unrecognized events;

establishing a new cluster for the second motion event candidate; and

associating the new cluster with the category for unrecognized events.

5. The method of claim 1, further comprising:

storing a respective cluster creation time, a respective current cluster weight, a respective current cluster center, and a respective current cluster radius for each of the one or more existing clusters; and

in accordance with the determination that the respective motion vector of the first motion event candidate of the series of motion event candidates falls within the respective range of the first existing cluster, updating the respective current cluster weight, the respective current cluster center, and the respective current cluster radius for the first existing cluster based on a spatial location of the respective motion vector of the first motion event candidate.

6. The method of claim 5, wherein, before the updating, the first existing cluster is associated with a category of unrecognized events, and wherein the method further comprises:

after the updating, determining a respective current cluster density for the first existing cluster based on the respective current cluster weight and the respective current cluster radius of the first existing cluster; and

in accordance with a determination that the respective current cluster density of the first existing cluster meets a predetermined cluster promotion density threshold, promoting the first existing cluster as a dense cluster, wherein promoting the first existing cluster further comprises;

creating a new event category for the first existing cluster; and

disassociating the first existing cluster from the category of unrecognized events.

7. The method of claim 6, further comprising:

after disassociating the first existing cluster from the category of unrecognized events, reassigning all motion vectors in the first existing cluster into the new event category created for the first existing cluster.

8. The method of claim 5, wherein, before the updating, the first existing cluster is associated with a category of unrecognized events, and wherein the method further comprises:

in accordance with a determination that the first existing cluster has included fewer than a threshold number of motion vectors for at least a threshold amount of time since the respective cluster creation time of the first existing cluster;

deleting the first existing cluster including all motion vectors currently in the first existing cluster; and

removing the motion event candidates corresponding to the deleted motion vectors from the category of unrecognized events.

9. The method of claim 5, wherein the first existing cluster is associated with a category of recognized events, and wherein the method further comprises:

in accordance with a determination that the first existing cluster has not been updated for at least a threshold amount of time, deleting the first existing cluster including all motion vectors currently in the first existing cluster;

removing the motion event candidates corresponding to the deleted motion vectors from the category of recognized events; and

deleting the category of recognized events.

10. The method of claim 1, wherein the respective motion vector for each of the series of motion event candidates includes a start location and an end location of a respective object in motion detected in a respective video segment associated with the motion event candidate.

11. The method of claim 10, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

receiving the respective motion vector for each of the series of motion event candidates in real-time from a camera capturing the live video stream as said each motion event candidate is detected in the live video stream by the camera.

12. A computing system for categorizing a motion event candidate, comprising:

one or more processors; and

memory having instructions stored thereon, the instructions, when executed by the one or more processors, cause the processors to perform operations comprising;

obtaining a respective motion vector for each of a series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream, comprising;

identifying at least one object in motion in a respective video segment associated with the motion event candidate;

determining a respective motion track of the at least one object in motion within a predetermined duration; and

generating the respective motion vector for the motion event candidate based on the determined respective motion track of the at least one object in motion;

in response to receiving the respective motion vector for each of the series of motion event candidates, determining a spatial relationship, in a vector event space, between the respective motion vector of said each motion event candidate to one or more existing clusters established based on a plurality of previously processed motion vectors; and

in accordance with a determination that the respective motion vector of a first motion event candidate of the series of motion event candidates falls within a respective spatial range of at least a first existing cluster of the one or more existing clusters, assigning the first motion event candidate to at least a first event category associated with the first existing cluster.

13. The computing system of claim 12, wherein the first event category is a category for unrecognized events.

14. The computing system of claim 12, wherein the first event category is a category for recognized events.

15. The computing system of claim 12, wherein the operations further comprise:

in accordance with a determination that the respective motion vector of a second motion event candidate of the series of motion event candidates falls beyond a respective range of any existing cluster;

assigning the second motion event candidate to a category for unrecognized events;

establishing a new cluster for the second motion event candidate; and

associating the new cluster with the category for unrecognized events.

16. The computing system of claim 12, wherein the operations further comprise:

storing a respective cluster creation time, a respective current cluster weight, a respective current cluster center, and a respective current cluster radius for each of the one or more existing clusters; and

in accordance with the determination that the respective motion vector of the first motion event candidate of the series of motion event candidates falls within the respective range of the first existing cluster, updating the respective current cluster weight, the respective current cluster center, and the respective current cluster radius for the first existing cluster based on a spatial location of the respective motion vector of the first motion event candidate.

17. The computing system of claim 16, wherein, before the updating, the first existing cluster is associated with a category of unrecognized events, and wherein the method further comprises:

after the updating, determining a respective current cluster density for the first existing cluster based on the respective current cluster weight and the respective current cluster radius of the first existing cluster; and

in accordance with a determination that the respective current cluster density of the first existing cluster meets a predetermined cluster promotion density threshold, promoting the first existing cluster as a dense cluster, wherein promoting the first existing cluster further comprises;

creating a new event category for the first existing cluster; and

disassociating the first existing cluster from the category of unrecognized events.

18. The computing system of claim 17, wherein the operations further comprise:

after disassociating the first existing cluster from the category of unrecognized events, reassigning all motion vectors in the first existing cluster into the new event category created for the first existing cluster.

19. The computing system of claim 16, wherein, before the updating, the first existing cluster is associated with a category of unrecognized events, and wherein the operations further comprise:

in accordance with a determination that the first existing cluster has included fewer than a threshold number of motion vectors for at least a threshold amount of time since the respective cluster creation time of the first existing cluster;

deleting the first existing cluster including all motion vectors currently in the first existing cluster; and

removing the motion event candidates corresponding to the deleted motion vectors from the category of unrecognized events.

20. The computing system of claim 16, wherein the first existing cluster is associated with a category of recognized events, and wherein the operations further comprise:

in accordance with a determination that the first existing cluster has not been updated for at least a threshold amount of time, deleting the first existing cluster including all motion vectors currently in the first existing cluster;

removing the motion event candidates corresponding to the deleted motion vectors from the category of recognized events; and

deleting the category of recognized events.

21. The computing system of claim 12, wherein the respective motion vector for each of the series of motion event candidates includes a start location and an end location of a respective object in motion detected in a respective video segment associated with the motion event candidate.

22. The computing system of claim 21, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

receiving the respective motion vector for each of the series of motion event candidates in real-time from a camera capturing the live video stream as said each motion event candidate is detected in the live video stream by the camera.

23. A method of categorizing a motion event candidate, comprising:

obtaining a respective motion vector for each of a series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream;

in response to receiving the respective motion vector for each of the series of motion event candidates, determining a spatial relationship between the respective motion vector of said each motion event candidate to one or more existing clusters established based on a plurality of previously processed motion vectors;

in accordance with a determination that the respective motion vector of a first motion event candidate of the series of motion event candidates falls within a respective range of at least a first existing cluster of the one or more existing clusters, assigning the first motion event candidate to at least a first event category associated with the first existing cluster;

storing a respective cluster creation time, a respective current cluster weight, a respective current cluster center, and a respective current cluster radius for each of the one or more existing clusters; and

in accordance with the determination that the respective motion vector of the first motion event candidate of the series of motion event candidates falls within the respective range of the first existing cluster, updating the respective current cluster weight, the respective current cluster center, and the respective current cluster radius for the first existing cluster based on a spatial location of the respective motion vector of the first motion event candidate, wherein the first existing cluster is associated with a category of unrecognized events before the updating; and

in accordance with a determination that the first existing cluster has included fewer than a threshold number of motion vectors for at least a threshold amount of time since the respective cluster creation time of the first existing cluster;

deleting the first existing cluster including all motion vectors currently in the first existing cluster; and

removing the motion event candidates corresponding to the deleted motion vectors from the category of unrecognized events.

24. The method of claim 23, further comprising:

in accordance with a determination that the respective motion vector of a second motion event candidate of the series of motion event candidates falls beyond a respective range of any existing cluster;

assigning the second motion event candidate to a category for unrecognized events;

establishing a new cluster for the second motion event candidate; and

associating the new cluster with the category for unrecognized events.

25. The method of claim 23, wherein the method further comprises:

after the updating, determining a respective current cluster density for the first existing cluster based on the respective current cluster weight and the respective current cluster radius of the first existing cluster; and

in accordance with a determination that the respective current cluster density of the first existing cluster meets a predetermined cluster promotion density threshold, promoting the first existing cluster as a dense cluster, wherein promoting the first existing cluster further comprises;

creating a new event category for the first existing cluster; and

disassociating the first existing cluster from the category of unrecognized events.

26. The method of claim 25, further comprising:

after disassociating the first existing cluster from the category of unrecognized events, reassigning all motion vectors in the first existing cluster into the new event category created for the first existing cluster.

27. The method of claim 23, wherein the respective motion vector for each of the series of motion event candidates includes a start location and an end location of a respective object in motion detected in a respective video segment associated with the motion event candidate.

28. The method of claim 27, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

receiving the respective motion vector for each of the series of motion event candidates in real-time from a camera capturing the live video stream as said each motion event candidate is detected in the live video stream by the camera.

29. The method of claim 23, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

identifying at least one object in motion in a respective video segment associated with the motion event candidate;

determining a respective motion track of the at least one object in motion within a predetermined duration; and

generating the respective motion vector for the motion event candidate based on the determined respective motion track of the at least one object in motion.

30. A computing system for categorizing a motion event candidate, comprising:

one or more processors; and

memory having instructions stored thereon, the instructions, when executed by the one or more processors, cause the processors to perform operations comprising;

obtaining a respective motion vector for each of a series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream;

in response to receiving the respective motion vector for each of the series of motion event candidates, determining a spatial relationship between the respective motion vector of said each motion event candidate to one or more existing clusters established based on a plurality of previously processed motion vectors;

in accordance with a determination that the respective motion vector of a first motion event candidate of the series of motion event candidates falls within a respective range of at least a first existing cluster of the one or more existing clusters, assigning the first motion event candidate to at least a first event category associated with the first existing cluster;

storing a respective cluster creation time, a respective current cluster weight, a respective current cluster center, and a respective current cluster radius for each of the one or more existing clusters; and

in accordance with the determination that the respective motion vector of the first motion event candidate of the series of motion event candidates falls within the respective range of the first existing cluster, updating the respective current cluster weight, the respective current cluster center, and the respective current cluster radius for the first existing cluster based on a spatial location of the respective motion vector of the first motion event candidate, wherein the first existing cluster is associated with a category of unrecognized events before the updating; and

in accordance with a determination that the first existing cluster has included fewer than a threshold number of motion vectors for at least a threshold amount of time since the respective cluster creation time of the first existing cluster;

deleting the first existing cluster including all motion vectors currently in the first existing cluster; and

removing the motion event candidates corresponding to the deleted motion vectors from the category of unrecognized events.

31. The computing system of claim 30, wherein the operations further comprise:

in accordance with a determination that the respective motion vector of a second motion event candidate of the series of motion event candidates falls beyond a respective range of any existing cluster;

assigning the second motion event candidate to a category for unrecognized events;

establishing a new cluster for the second motion event candidate; and

associating the new cluster with the category for unrecognized events.

32. The computing system of claim 30, wherein the operations further comprise:

after the updating, determining a respective current cluster density for the first existing cluster based on the respective current cluster weight and the respective current cluster radius of the first existing cluster; and

in accordance with a determination that the respective current cluster density of the first existing cluster meets a predetermined cluster promotion density threshold, promoting the first existing cluster as a dense cluster, wherein promoting the first existing cluster further comprises;

creating a new event category for the first existing cluster; and

disassociating the first existing cluster from the category of unrecognized events.

33. The computing system of claim 32, wherein the operations further comprise:

after disassociating the first existing cluster from the category of unrecognized events, reassigning all motion vectors in the first existing cluster into the new event category created for the first existing cluster.

34. The computing system of claim 30, wherein the respective motion vector for each of the series of motion event candidates includes a start location and an end location of a respective object in motion detected in a respective video segment associated with the motion event candidate.

35. The computing system of claim 34, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

receiving the respective motion vector for each of the series of motion event candidates in real-time from a camera capturing the live video stream as said each motion event candidate is detected in the live video stream by the camera.

36. The computing system of claim 30, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

identifying at least one object in motion in a respective video segment associated with the motion event candidate;

determining a respective motion track of the at least one object in motion within a predetermined duration; and

generating the respective motion vector for the motion event candidate based on the determined respective motion track of the at least one object in motion.

37. A method of categorizing a motion event candidate, comprising:

obtaining a respective motion vector for each of a series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream;

in response to receiving the respective motion vector for each of the series of motion event candidates, determining a spatial relationship between the respective motion vector of said each motion event candidate to one or more existing clusters established based on a plurality of previously processed motion vectors;

in accordance with a determination that the respective motion vector of a first motion event candidate of the series of motion event candidates falls within a respective range of at least a first existing cluster of the one or more existing clusters, assigning the first motion event candidate to at least a first event category associated with the first existing cluster;

storing a respective cluster creation time, a respective current cluster weight, a respective current cluster center, and a respective current cluster radius for each of the one or more existing clusters; and

in accordance with the determination that the respective motion vector of the first motion event candidate of the series of motion event candidates falls within the respective range of the first existing cluster, updating the respective current cluster weight, the respective current cluster center, and the respective current cluster radius for the first existing cluster based on a spatial location of the respective motion vector of the first motion event candidate, wherein the first existing cluster is associated with a category of recognized events before the updating; and

in accordance with a determination that the first existing cluster has not been updated for at least a threshold amount of time, deleting the first existing cluster including all motion vectors currently in the first existing cluster;

removing the motion event candidates corresponding to the deleted motion vectors from the category of recognized events, and not assigning the motion event candidates to a category for unrecognized events; and

deleting the category of recognized events.

38. The method of claim 37, further comprising:

in accordance with a determination that the respective motion vector of a second motion event candidate of the series of motion event candidates falls beyond a respective range of any existing cluster;

assigning the second motion event candidate to a category for unrecognized events;

establishing a new cluster for the second motion event candidate; and

associating the new cluster with the category for unrecognized events.

39. The method of claim 37, wherein the respective motion vector for each of the series of motion event candidates includes a start location and an end location of a respective object in motion detected in a respective video segment associated with the motion event candidate.

40. The method of claim 39, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

receiving the respective motion vector for each of the series of motion event candidates in real-time from a camera capturing the live video stream as said each motion event candidate is detected in the live video stream by the camera.

41. The method of claim 37, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

identifying at least one object in motion in a respective video segment associated with the motion event candidate;

determining a respective motion track of the at least one object in motion within a predetermined duration; and

generating the respective motion vector for the motion event candidate based on the determined respective motion track of the at least one object in motion.

42. A computing system for categorizing a motion event candidate, comprising:

one or more processors; and

memory having instructions stored thereon, the instructions, when executed by the one or more processors, cause the processors to perform operations comprising;

obtaining a respective motion vector for each of a series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream;

in response to receiving the respective motion vector for each of the series of motion event candidates, determining a spatial relationship between the respective motion vector of said each motion event candidate to one or more existing clusters established based on a plurality of previously processed motion vectors;

in accordance with a determination that the respective motion vector of a first motion event candidate of the series of motion event candidates falls within a respective range of at least a first existing cluster of the one or more existing clusters, assigning the first motion event candidate to at least a first event category associated with the first existing cluster;

storing a respective cluster creation time, a respective current cluster weight, a respective current cluster center, and a respective current cluster radius for each of the one or more existing clusters; and

in accordance with the determination that the respective motion vector of the first motion event candidate of the series of motion event candidates falls within the respective range of the first existing cluster, updating the respective current cluster weight, the respective current cluster center, and the respective current cluster radius for the first existing cluster based on a spatial location of the respective motion vector of the first motion event candidate, wherein the first existing cluster is associated with a category of recognized events before the updating; and

in accordance with a determination that the first existing cluster has not been updated for at least a threshold amount of time, deleting the first existing cluster including all motion vectors currently in the first existing cluster;

removing the motion event candidates corresponding to the deleted motion vectors from the category of recognized events, and not assigning the motion event candidates to a category for unrecognized events; and

deleting the category of recognized events.

43. The computing system of claim 42, wherein the operations further comprise:

in accordance with a determination that the respective motion vector of a second motion event candidate of the series of motion event candidates falls beyond a respective range of any existing cluster;

assigning the second motion event candidate to a category for unrecognized events;

establishing a new cluster for the second motion event candidate; and

associating the new cluster with the category for unrecognized events.

44. The computing system of claim 42, wherein the respective motion vector for each of the series of motion event candidates includes a start location and an end location of a respective object in motion detected in a respective video segment associated with the motion event candidate.

45. The computing system of claim 44, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

receiving the respective motion vector for each of the series of motion event candidates in real-time from a camera capturing the live video stream as said each motion event candidate is detected in the live video stream by the camera.

46. The computing system of claim 42, wherein obtaining the respective motion vector for each of the series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream further comprises:

identifying at least one object in motion in a respective video segment associated with the motion event candidate;

determining a respective motion track of the at least one object in motion within a predetermined duration; and

generating the respective motion vector for the motion event candidate based on the determined respective motion track of the at least one object in motion.