Video segmentation using statistical pixel modeling

• US 20040151374A1
• Filed: 09/22/2003
• Published: 08/05/2004
• Est. Priority Date: 03/23/2001
• Status: Active Grant

• Current Assignee: Motorola Solutions, Inc.
• Original Assignee: ObjectVideo Incorporated (Alarm.com Holdings, Inc.)
• Inventors: Haering, Niels, Lipton, Alan J., Severson, William E., Allmen, Mark C., Strat, Thomas M.
• Granted Patent: US 7,224,852 B2
• Time in Patent Office: Days
• US Class Current: 382/181
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30241 Trajectory
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation
• CPC Class Codes: G06T 7/277 involving stochastic approaches, e.g. using Kalman filters
• CPC Class Codes: G06V 10/26 Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
• CPC Class Codes: G06V 10/28 Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
• 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: G06V 2201/01 Solutions for problems related to non-uniform document background
• CPC Class Codes: G08B 13/196 using television cameras
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

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.

Claims

1. 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; and

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.

2. A computer-readable medium comprising software implementing the method of claim 1.

3. An intelligent video surveillance system comprising a computer system comprising:

a computer; and

a computer-readable medium according to claim 2.

4. 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.

5. A computer-readable medium comprising software implementing the method of claim 4.

6. An intelligent video surveillance system comprising a computer system comprising:

a computer; and

a computer-readable medium according to claim 5.

7. 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 and a secondary 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.

8. A computer-readable medium comprising software implementing the method of claim 7.

9. An intelligent video surveillance system comprising a computer system comprising:

a computer; and

a computer-readable medium according to claim 8.

10. A method of implementing an intelligent video surveillance system, comprising:

segmenting video into foreground and background components, the segmenting comprising;

obtaining a sequence of video frames;

building and updating at least one background statistical model for each region of the video frames, based on the video frames; and

assigning labels to the regions, based on the at least one background statistical model;

identifying and classifying objects based on the labeled regions; and

analyzing behaviors of at least one object.

11. A computer-readable medium comprising software implementing the method of claim 10.

12. An intelligent video surveillance system comprising a computer system comprising:

a computer; and

a computer-readable medium according to claim 11.