Hough transform based motion detection image recording system and method

• US 7,035,434 B2
• Filed: 12/03/2001
• Issued: 04/25/2006
• Est. Priority Date: 12/11/2000
• Status: Active Grant

• Current Assignee: Texas Instruments, Inc.
• Original Assignee: Texas Instruments, Inc.
• Inventors: Estevez, Leonardo W.
• Primary Examiner(s): Wu, Jingge
• Assistant Examiner(s): Tabatabai, Abolfazl
• Application Number:
  US09/998,132
• Publication Number:
  US 20020071596A1
• Time in Patent Office: 1,604 Days
• Field of Search: 382/103, 382/107, 382/146, 382/166, 382/169, 382/171, 382/173, 382/287, 382/168, 382/281, 342/20, 342/28, 342/90, 342/94, 348/94, 348/152, 348/154, 348/139, 348/155, 348/672, 375/240.18, 375/258, 375/317, 358/522, 708/5, 708/400, 708/820, 352/39
• US Class Current: 382/107
• CPC Class Codes: G06T 7/262 using transform domain methods, e.g. Fourier domain methods

Abstract

A Hough based motion detection image recording system includes an image sensor operable to view a surveillance site and output real-time image data corresponding to the site. The system also includes a processor operable to receive the image data from the image sensor and a memory coupled to the processor. The system includes a transformation application residing in the memory and executable by the processor. The transformation application is operable to detect a real-time feature segment associated with the site from the image data. The system further includes an analysis engine residing in the memory and executable by the processor. The analysis engine is operable to compare the real-time feature segment with a baseline feature segment and initiate recording of the image data if the feature segment comparison exceeds a predetermined criteria.

Claims

1. A method for motion detection image recording, comprising:

generating a baseline feature segment associated with a surveillance site;

generating a baseline histogram associated with the baseline feature segment;

receiving real-time image data corresponding to the surveillance site;

detecting a real-time feature segment associated with the surveillance site from the real-time image data;

generating a real-time histogram associated with the real-time feature segment;

comparing the real-time feature segment with the baseline feature segment by comparing the baseline histogram to the real-time histogram; and

recording the real-time image data if the comparison of the real-time feature segment with the baseline feature segment exceeds a feature segment criteria.

2. The method of claim 1, wherein detecting a real-time feature segment further comprises transforming the real-time image data using a Hough routine to extract the real-time feature segment from the real-time image data.

3. The method of claim 1, wherein generating a baseline feature segment further comprises:

receiving baseline image data corresponding to the surveillance site;

detecting a plurality of feature segments corresponding to the surveillance site from the baseline image data; and

identifying one of the plurality of feature segments as the baseline feature segment.

4. The method of claim 1, wherein comparing the real-time feature segment further comprises determining whether an occlusion is present in the real-time feature segment relative to the baseline feature segment, and wherein recording the real-time image data comprises recording the real-time image data if the occlusion exceeds the feature segment criteria.

5. The method of claim 1, wherein receiving real-time image data comprises:

generating analog image data via an analog camera;

converting the analog image data to digital image data; and

transmitting the digital image data to a processor to detect the real-time feature segment.

6. The method of claim 1, wherein generating a baseline feature segment further comprises:

receiving baseline image data corresponding to the surveillance site;

detecting a plurality of feature segments associated with the surveillance site from the baseline image data; and

identifying a subset of the plurality of feature segments as the baseline feature segment.

7. The method of claim 1, wherein comparing the real-time feature segment to the baseline feature segment further comprises:

determining a length of the baseline feature segment;

determining a length of the corresponding real-time feature segment; and

determining whether a difference between the length of the baseline feature segment and the length of the real-time feature segment exceeds the feature segment criteria.

8. A method for image recording, comprising:

generating baseline feature segments corresponding to a surveillance site;

generating a baseline histogram corresponding to the baseline feature segments;

receiving real-time image data corresponding to the surveillance site;

generating real-time feature segments associated with the surveillance site from the real-time image data;

generating a real-time histogram corresponding to the real-time feature segments;

determining whether an occlusion is present in one or more of the real-time feature segments by comparing the baseline histogram with the real-time histogram; and

recording the real-time image data if the occlusion is present in one or more of the real-time feature segments.

9. The method of claim 8, further comprising:

receiving feature segment criteria;

determining whether the occlusion exceeds the feature segment criteria; and

wherein recording comprises recording the real-time image data if the occlusion exceeds the feature segment criteria.

10. The method of claim 8, wherein detecting the real-time feature segments further comprises applying a Hough routine to the real-time image data to extract the real-time feature segments from the real-time image data.

11. The method of claim 8, further comprising recording the real-time image data if a quantity of the real-time feature segments exceeds a quantity of the baseline feature segments.

12. A method for motion detection image recording, comprising:

generating a baseline feature segment associated with a surveillance site;

determining a length of the baseline feature segment;

receiving real-time image data corresponding to the surveillance site;

detecting a real-time feature segment associated with the surveillance site from the real-time image data;

determining a length of the corresponding real-time feature segment;

comparing the real-time feature segment with the baseline feature segment by determining whether a difference between the length of the baseline feature segment and the length of the real-time feature segment exceeds a feature segment criteria; and

recording the real-time image data if the comparison of the real-time feature segment with the baseline feature segment exceeds a feature segment criteria.

13. The method of claim 12, wherein detecting a real-time feature segment further comprises transforming the real-time image data using a Hough routine to extract the real-time feature segment from the real-time image data.

14. The method of claim 12, wherein generating a baseline feature segment further comprises:

receiving baseline image data corresponding to the surveillance site;

detecting a plurality of feature segments corresponding to the surveillance site from the baseline image data; and

identifying one of the plurality of feature segments as the baseline feature segment.

15. The method of claim 12, wherein comparing the real-time feature segment further comprises determining whether an occlusion is present in the real-time feature segment relative to the baseline feature segment, and wherein recording the real-time image data comprises recording the real-time image data if the occlusion exceeds the feature segment criteria.

16. The method of claim 12, wherein receiving real-time image data comprises:

generating analog image data via an analog camera;

converting the analog image data to digital image data; and

transmitting the digital image data to a processor to detect the real-time feature segment.

17. The method of claim 12, wherein generating a baseline feature segment further comprises:

receiving baseline image data corresponding to the surveillance site;

detecting a plurality of feature segments associated with the surveillance site from the baseline image data; and

identifying a subset of the plurality of feature segments as the baseline feature segment.

18. The method of claim 12, wherein comparing the real-time feature segment with the baseline feature segment further comprises:

generating a baseline histogram associated with the baseline feature segment;

generating a real-time histogram associated with the real-time feature segment; and

comparing the baseline histogram to the real-time histogram.

19. A method for image recording, comprising:

generating baseline feature segments corresponding to a surveillance site;

receiving real-time image data corresponding to the surveillance site;

generating real-time feature segments associated with the surveillance site from the real-time image data;

determining whether an occlusion is present in one or more of the real-time feature segments; and

in one or more of the real-time feature segments and a quantity of the real-time feature segments exceeds a quantity of the baseline feature segments.

20. The method of claim 19, further comprising:

receiving feature segment criteria;

determining whether the occlusion exceeds the feature segment criteria; and

wherein recording comprises recording the real-time image data if the occlusion exceeds the feature segment criteria.

21. The method of claim 19, wherein detecting the real-time feature segments further comprises applying a Hough routine to the real-time image data to extract the real-time feature segments from the real-time image data.

22. The method of claim 19, further comprising:

generating a baseline histogram corresponding to the baseline feature segments;

generating a real-time histogram corresponding to the real-time feature segments; and

said step of determing whether an occlusion is present in one or more of the real-time feature segments includes comparing the baseline histogram with the real-time histogram.