Patent US6130129A successfully refreshed from Google Patents.
Patent US7224852B2 successfully refreshed from Google Patents.
US7224852B2
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Video segmentation using statistical pixel modeling
Assignee: Motorola Solutions Inc
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Filing: 2003-09-22
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Granted: 2007-05-29
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Expires: 2023-09-22
Abstract
Abstract A method for segmenting video data into foreground and background portions utilizes statistical modeling of the pixels. A statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons. The method for segmenting video data may be further incorporated into a method for implementing an intelligent video surveillance system.
Inventors
- 👤 Alan J. Lipton
- 👤 Mark C. Allmen
- 👤 Niels Haering
- 👤 William E. Severson
- 👤 Thomas M. Strat
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 11:46
CPC Classifications
G; G08; G08B; G08B13/00; G08B13/18; G08B13/189; G08B13/194; G08B13/196; G06; G06T; G06T7/00; G06T7/10; G06T7/11; G06T7/143; G06T7/194; G06T7/20; G06T7/277; G06V; G06V10/00; G06V10/20; G06V10/26; G06V10/28; G06V20/00; G06V20/50; G06V20/52; G08B31/00; G06T2207/00; G06T2207/10; G06T2207/10016; G06T2207/30; G06T2207/30241; G06V2201/00; G06V2201/01
Claims Analysis
Total Claims: 7
Claim 1
Independent Claim
109 words
A method of implementing an intelligent video surveillance system, comprising: obtaining a frame sequence from an input video stream; executing a first-pass method for each frame of the frame sequence, the first-pass method comprising the steps of: aligning the frame with a scene model; and updating a background statistical model; finalizing the background statistical model; executing a second-pass method for each frame of the frame sequence, the second-pass method comprising the steps of: labeling each region of the frame; and performing spatial/temporal filtering of the regions of the frame; identifying and classifying objects using the labeled and filtered regions; and analyzing behaviors of at least one of the objects.
Claim 2
Independent Claim
109 words
A method of implementing an intelligent video surveillance system, comprising: obtaining a frame sequence from an input video stream; executing a first-pass method for each frame of the frame sequence, the first-pass method comprising the steps of: aligning the frame with a scene model; and updating a background statistical model; finalizing the background statistical model; executing a second-pass method for each frame of the frame sequence, the second-pass method comprising the steps of: labeling each region of the frame; and performing spatial/temporal filtering of the regions of the frame; identifying and classifying objects using the labeled and filtered regions; and analyzing behaviors of at least one of the objects.
Claim 3
Dependent Claim
12 words
A computer-readable medium comprising software implementing the method of claim 1 .
Claim 4
Dependent Claim
21 words
An intelligent video surveillance system comprising a computer system comprising: a computer; and a computer-readable medium according to claim 2 .
Claim 5
Independent Claim
72 words
A method of implementing an intelligent video surveillance system, comprising: obtaining a frame sequence from a video stream; for each frame in the frame sequence, performing the following steps: aligning the frame with a scene model; building a background statistical model; labeling the regions of the frame; and performing spatial/temporal filtering; identifying and classifying objects based on the results of the labeling and filtering; and analyzing behaviors of at least one object.
Claim 6
Independent Claim
72 words
A method of implementing an intelligent video surveillance system, comprising: obtaining a frame sequence from a video stream; for each frame in the frame sequence, performing the following steps: aligning the frame with a scene model; building a background statistical model; labeling the regions of the frame; and performing spatial/temporal filtering; identifying and classifying objects based on the results of the labeling and filtering; and analyzing behaviors of at least one object.
Claim 7
Dependent Claim
12 words
A computer-readable medium comprising software implementing the method of claim 4 .
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2010-05-28 | 2010-11-27 | 2011-05-29 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2014-05-28 | 2014-11-27 | 2015-05-29 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2018-05-28 | 2018-11-27 | 2019-05-29 | $7700.00 | Window Closed / Lapsed |