SYSTEM AND METHOD FOR MANAGING VIDEO STORAGE ON A VIDEO SURVEILLANCE SYSTEM

• US 20100208064A1
• Filed: 07/02/2009
• Published: 08/19/2010
• Est. Priority Date: 02/19/2009
• Status: Abandoned Application

• Current Assignee: Panasonic Intellectual Property Management Co., Ltd. (Panasonic Holdings Corporation)
• Original Assignee: Panasonic Corporation (Panasonic Holdings Corporation)
• Inventors: Liu, Lipin, Ozdemir, Hasan Timucin, Lee, Kuo Chu
• Application Number:
  US12/496,757
• Publication Number:
  US 20100208064A1
• Time in Patent Office: Days
• US Class Current: 348/143
• CPC Class Codes: G08B 13/19667 Details realated to data compression, encryption or encoding, e.g. resolution modes for reducing data volume to lower transmission bandwidth or memory requirements
• CPC Class Codes: H04H 60/37 for identifying segments of broadcast information, e.g. scenes or extracting programme ID
• CPC Class Codes: H04N 21/4223 Cameras H04N23/00 takes precedence
• CPC Class Codes: H04N 21/42661 for reading from or writing on a magnetic storage medium, e.g. hard disk drive
• CPC Class Codes: H04N 21/4334 Recording operations recording of a television signal H04N5/76; arrangements for recording or accumulating broadcast information or broadcast-related information H04H60/27
• CPC Class Codes: H04N 21/4335 Housekeeping operations, e.g. prioritizing content for deletion because of storage space restrictions storage management, e.g. defragmentation G06F3/0604; unloading stored programs G06F9/445; storage management in file systems G06F16/10; buffering arrangements in a network node or in an end terminal in packet networks H04L49/90
• CPC Class Codes: H04N 21/44008 involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/4402 involving reformatting operations of video signals for household redistribution, storage or real-time display details of conversion of video standards at pixel level H04N7/01; video transcoding H04N19/40; adapting incoming signals to the display format of the display terminal G09G5/005; media handling at the source in data packet switching networks H04L65/764
• CPC Class Codes: H04N 21/8456 by decomposing the content in the time domain, e.g. in time segments
• CPC Class Codes: H04N 5/781 on disks or drums
• CPC Class Codes: H04N 7/181 for receiving images from a plurality of remote sources

Abstract

A system and method for managing video storage on a video surveillance system is disclosed. The system calculates an importance score for a video segment based on a weighted average of multiple scores corresponding to the video event. The multiple scores include an event correlation score correlating a video event with a plurality of other video events, an abnormality score indicating the abnormality of the observed event, a user entered score, a score relating to the number of times a specific location was visited by a moving object, the amount of times a video has been retrieved, and a predicted future storage space. The importance score may be used to determine a video retention operation, such as retaining the video event, purging the video event, reducing the video quality of the event, or storing the video in mix-reality format.

Claims

1. A system for managing a plurality of stored video segments corresponding to video events captured by a video surveillance system comprising:

a video data store that stores the plurality of video segments;

a scoring module that generates an importance score based on an event correlation score corresponding to a correlation between a video segment and other video segments having corresponding video events that correlate spatially and temporally to a video event corresponding to the video segment to be scored; and

a video management module that performs a video retention operation on the given video segment based in part on the importance score generated by scoring module.

2. The system of claim 1 wherein the event correlation score is based on a ratio corresponding to distances between an object observed in the video segment and objects observed in the other video segments and maximum possible distances between objects observed in the video segment and objects observed in the other video segments.

3. The system of claim 1 wherein the event correlation score is based on a ratio corresponding to durations of the other video segments and an amount of time corresponding to a duration of all the video segments.

4. The system of claim 1 further comprises a behavior assessment module that generates a behavior score corresponding to a video event, wherein a behavior score indicates a degree of conformity of the video event with at least one motion model defining accepted motion.

5. The system of claim 4 wherein the event correlation score is further based in part on a correlation of a behavior score of the video segment and behavior scores of the other video segments.

6. The system of claim 1 wherein the event correlation score is further based in part on whether an object in the video segment appears in the other video segments.

7. The system of claim 4 wherein the importance score is further based on the behavior score of the video segment.

8. The system of claim 1 wherein the importance score is further based on an amount of instances that the video segment has been retrieved from the video data store.

9. The system of claim 1 wherein the importance score is further based on an amount of time the video segment has been stored in the video data store.

10. The system of claim 1 wherein the importance score is further based on a user score of the video segment corresponding to a user'"'"'s assessment of the video segment.

11. The system of claim 1 wherein the importance score is further based on a ratio between an of amount of times an object moves to one or more predefined target areas and a total amount of times the predefined target areas were visited.

12. The system of claim 1 wherein the importance score is further based on a predicted amount of storage required for a video event.

13. The system of claim 1 wherein the video retention operation is purging the video segment from the data store.

14. The system of claim 1 wherein the video retention operation is retaining the video segment in the data store.

15. The system of claim 1 wherein the video retention operation is reducing video quality of the video segment, wherein a size of the video segment decreases as a result of reducing the video quality of the video segment.

16. The system of claim 1 further comprising a mixed reality module operable to generate a mixed reality video using one or more video segments, wherein the generation of the mixed reality video is based in part on the importance score of the one or more video segments.

17. The system of claim 1 further comprising a learning module that adjusts the scoring module based on statistics corresponding to decisions of a user of the system.

18. The system of claim 1 wherein the video segment is received from a first camera and the other video segments are received from at least one other camera.

19. The system of claim 1 wherein the importance score is further based on a weighted average of

the event correlation score;

a behavior score corresponding to the video event indicating a degree of conformity of the video event with at least one motion model defining accepted motion;

an amount of instances when the video segment has been retrieved from the video data store;

an amount of time the video segment has been in the video data store;

a user score of the video segment corresponding to a user assessment of the video segment;

a ratio between an of amount of times an object moves to one or more predefined target areas and a total amount of times the predefined target areas were visited; and

a predicted amount of storage required for a video event.

20. A system for managing a plurality of stored video segments corresponding to video events captured by a video surveillance system comprising:

a video data store that stores the plurality of video segments;

a scoring module that generates an importance score based on a weighted average of at least two or more of the following;

1) an event correlation score corresponding to a correlation between a video segment and other video segments having corresponding video events that correlate spatially and temporally to a video event corresponding to the video segment to be scored;

2) a behavior score corresponding to the video event, wherein a behavior score indicates a degree of conformity of the video event with at least one motion model defining accepted motion;

3) an amount of instances that the video segment has been retrieved from the data store;

4) a user score of the video segment corresponding to a user assessment of the video segment;

5) a ratio between an of amount of times an object moves to one or more predefined target areas and a total amount of times the predefined target areas were visited; and

6) a predicted amount of storage required for a video event; and

a video management module that performs a video retention operation on the given video segment based in part on the importance score generated by scoring module.

21. The system of claim 20 wherein the event correlation score is based on a ratio corresponding to distances between an object observed in the video segment and objects observed in the other video segments and maximum possible distances between objects observed in the video segment and objects observed in the other video segments.

22. The system of claim 20 wherein the event correlation score is based on a ratio corresponding to durations of the other video segments and an amount of time corresponding to a duration of all the video segments.

23. The system of claim 20 wherein the event correlation score is further based in part on a correlation of the behavior score of the video segment and the behavior score of the other video segments.

24. The system of claim 20 wherein the event correlation score is further based in part on whether an object in the video segment appears in the other video segments.