Video segmentation using statistical pixel modeling
AssigneeDiamondBack Vision, Inc.
InventorsAlan J. Lipton, Mark Allmen, Niels Haering, William Severson, Thomas M. Strat
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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.
Claims60
A two-pass method of video segmentation, for differentiating between foreground and background portions of video, comprising the steps of:
- 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.
A one-pass method of video segmentation, for differentiating between foreground and background portions of video, comprising the steps of:
- obtaining a frame sequence from a video stream;
- and
- 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.
A one-pass method of video segmentation, for differentiating between foreground and background portions of video, comprising the steps of:
- obtaining a frame sequence from a video stream;
- and
- 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.
A two-pass method of video segmentation, for differentiating between foreground and background portions of video, comprising the steps of:
- 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, the background statistical model comprising values corresponding to regions of frames of the frame sequence and variances for the regions;
- finalizing the background statistical model;
- and
- 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.
A one-pass method of video segmentation, for differentiating between foreground and background portions of video, comprising the steps of:
- obtaining a frame sequence from a video stream;
- and
- for each frame in the frame sequence, performing the following steps:
- aligning the frame with a scene model;
- building a background statistical model, the background statistical model comprising values corresponding to regions of frames of the frame sequence and variances for the regions;
- labeling the regions of the frame;
- and
- performing spatial/temporal filtering.
Dependencies are inferred from the claim text.