STATIONARY TARGET DETECTION BY EXPLOITING CHANGES IN BACKGROUND MODEL

• US 20150146929A1
• Filed: 01/30/2015
• Published: 05/28/2015
• Est. Priority Date: 09/04/2007
• Status: Active Grant

• Current Assignee: Motorola Solutions, Inc.
• Original Assignee: Peter L. Venetianer
• Inventors: Hassan-Shafique, Khurram, Venetianer, Pter L., Yu, Li, Liu, Haiying
• Granted Patent: US 9,792,503 B2
• Time in Patent Office: Days
• 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

Provided is a computer-implemented method for processing one or more video frames. The meth can include generating, by a processor, a change in value of one or more pixels obtained from the one or more video frames; classifying, by the processor, the change in value of the one or more pixels to produce one or more classes of the change in value of the one or more pixels, wherein the one or more classes include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and constructing, by the processor, a listing of detected targets based on the one or more classes.

Claims

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

generating, by a processor, a change in value of one or more pixels obtained from the one or more video frames;

classifying, by the processor, the change in value of the one or more pixels to produce one or more classes of the change in value of the one or more pixels, wherein the one or more classes include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and

constructing, by the processor, a listing of detected targets based on the one or more classes.

2. The computer-implemented method of claim 1, further comprising:

constructing a first background model using the one or more video frames based on a first parameter; and

constructing a second background model using the one or more video frames based on a second parameter that is different from the first parameter.

3. The computer-implemented method of claim 2, wherein classifying the change in value of the one or more pixels comprises:

identifying a difference between the first background model and the second background model.

4. The computer-implemented method of claim 1, wherein classifying further comprises:

determining that the change in value of the one or more pixels represents one or more stationary targets based on a length of time of insertion for one or more target insertions.

5. The computer-implemented method of claim 1, further comprising:

reclassifying at least one of the one or more classes of the change in value of the one or more pixels from one class to another class.

6. The computer-implemented method of claim 1, further comprising:

filtering the change in value of the one or more pixels based on a predetermined threshold; and

ignoring the change in value of the one or ore pixels that do not meet the predetermined threshold.

7. A device for processing one or more video frames, the device comprising:

a memory containing instructions; and

at least one processor, operably connected to the memory, that executes the instructions to perform operations comprising;

generating, by a processor, a change in value of one or more pixels obtained from the one or more video frames;

classifying, by the processor, the change in value of the one or more pixels to produce one or more classes of the change in value of the one or more pixels, wherein the one or more classes include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and

constructing, by the processor, a listing of detected targets based on the one or more classes.

8. The device of claim 7, wherein the at least one processor executes the instruction to perform operations further comprising:

constructing a first background model using the one or more video frames based on a first parameter; and

constructing a second background model using the one or more video frames based on a second parameter that is different from the first parameter.

9. The device of claim 8, wherein classifying the change in value of the one or more pixels comprises:

identifying a difference between the first background model and the second background model.

10. The device of claim 7, wherein classifying further comprises:

determining that the change in value of the one or more pixels represents one or more stationary targets based on a length of time of insertion for one or more target insertions.

11. The device of claim 7, wherein the at least one processor executes the instruction to perform operations further comprising:

reclassifying at least one of the one or more classes of the change in value of the one or more pixels from one class to another class.

12. The device of claim 7, wherein the at least one processor executes the instruction to perform operations further comprising:

filtering the change in value of the one or more pixels based on a predetermined threshold; and

ignoring the change in value of the one or ore pixels that do not meet the predetermined threshold.

13. A non-transitory computer-readable storage medium containing instructions which, when executed on a processor, perform a method for processing one or more video frames, the method comprising:

generating, by a processor, a change in value of one or more pixels obtained from the one or more video frames;

classifying, by the processor, the change in value of the one or more pixels to produce one or more classes of the change in value of the one or more pixels, wherein the one or more classes include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and

constructing, by the processor, a listing of detected targets based on the one or more classes.

14. The non-transitory computer-readable storage medium of claim 13, further comprising:

constructing a first background model using the one or more video frames based on a first parameter; and

constructing a second background model using the one or more video frames based on a second parameter that is different from the first parameter.

15. The non-transitory computer-readable storage medium of claim 14, wherein classifying the change in value of the one or more pixels comprises:

identifying a difference between the first background model and the second background model.

16. The non-transitory computer-readable storage medium of claim 13, wherein classifying further comprises:

determining that the change in value of the one or more pixels represents one or more stationary targets based on a length of time of insertion for one or more target insertions.

17. The non-transitory computer-readable storage medium of claim 13, further comprising:

reclassifying at least one of the one or more classes of the change in value of the one or more pixels from one class to another class.

18. The non-transitory computer-readable storage medium of claim 13, further comprising:

filtering the change in value of the one or more pixels based on a predetermined threshold; and

ignoring the change in value of the one or ore pixels that do not meet the predetermined threshold.