Stationary target detection by exploiting changes in background model

• US 8,948,458 B2
• Filed: 08/02/2013
• Issued: 02/03/2015
• Est. Priority Date: 09/04/2007
• Status: Active Grant

• Current Assignee: Motorola Solutions, Inc.
• Original Assignee: ObjectVideo Incorporated (Alarm.com Holdings, Inc.)
• Inventors: Hassan-Shafique, Khurram, Venetianer, Pter L., Yu, Li, Liu, Haiying
• Primary Examiner(s): Kassa, Yosef
• Application Number:
  US13/957,636
• Publication Number:
  US 20130315444A1
• Time in Patent Office: 550 Days
• Field of Search: 382/103, 382/224, 382/278, 382/282, 348/143, 348/154, 348/161
• US Class Current: 382/103
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 18/24 Classification techniques
• 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: G08B 13/19602 Image analysis to detect motion of the intruder, e.g. by frame subtraction

Abstract

A computer-implemented method for processing one or more video frames may include obtaining one or more video frames; generating one or more blobs using the one or more video frames; classifying the one or more blobs to produce one or more classified blobs, wherein the one or more classified blobs include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and constructing a list of detected targets based on the one or more classified blobs.

Claims

1. A computer-implemented method for processing one or more video frames, comprising:

obtaining, by a computer, one or more video frames;

generating, by the computer, one or more blobs using the one or more video frames;

classifying, by the computer, the one or more blobs to produce one or more classified blobs, wherein the one or more classified blobs include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and

constructing a list of detected targets based on the one or more classified blobs.

2. The method of claim 1, further comprising:

determining, by the computer, one or more stationary targets based on a length of time of insertion for the one or more target insertions.

3. The method of claim 1, further comprising:

adding a blob from the one or more blobs to the list of detected targets when the blob is not in the list of detected targets.

4. The method of claim 1, further comprising:

classifying one or more blobs in the list of detected targets as one or more local changes; and

removing the one or more blobs classified as one or more local changes from the list of detected targets.

5. The method of claim 1, further comprising:

reclassifying at least one of the one or more classified blobs.

6. The method of claim 5, further comprising:

updating the list of detected targets in response to the reclassifying of at least one of the one or more classified blobs.

7. The method of claim 5, wherein the reclassifying step further comprises changing the classification of the at least one of the one or more classified blobs from a target insertion to a stationary target.

8. The method of claim 1, further comprising:

comparing the one or more blobs with the list of detected targets to detect one or more target matches; and

determining if the one or more blobs matches the one or more target matches based on the comparison to the list of detected targets.

9. The method of claim 8, further comprising:

detecting one or more stationary targets based on one or more stationary thresholds.

10. The method of claim 8, further comprising:

generating one or more alerts regarding the one or more determined stationary targets.

11. The method of claim 1, further comprising:

confirming one or more stationary targets from the list of detected targets based on one or more stationary thresholds.

12. The method of claim 1, further comprising:

generating one or more alerts in response to classifying the one or more blobs.

13. The method of claim 1, further comprising:

generating, by the computer, a background model using the one or more video frames; and

generating, by the computer, a foreground model using the one or more video frames.

14. The method of claim 1, further comprising:

generating, by the computer, more than one background model using the one or more video frames.

15. The method of claim 14, further comprising:

generating, by the computer, a foreground model using the one or more video frames.

16. A system for processing one or more video frames, the system comprising:

a computer adapted to obtain one or more video frames, wherein the computer is adapted to;

generate one or more blobs using the one or more video frames;

classify the one or more blobs to produce one or more classified blobs, wherein the one or more classified blobs include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and

construct a list of detected targets based on the one or more classified blobs.

17. The system of claim 16, further comprising:

a sensor connected to the computer.

18. The system of claim 16, wherein the computer is further adapted to determine one or more stationary targets based on a length of time of insertion for the one or more target insertions.

19. A system for processing one or more video frames, the system comprising:

a computer adapted to obtain one or more video frames, wherein the computer is adapted to;

generate a first background model using the one or more video frames;

generate a second background model using the one or more video frames;

compare the first background model and the second background model to generate a comparison; and

generate one or more blobs in response to the comparison.