Visual motion analysis method for detecting arbitrary numbers of moving objects in image sequences
First Claim
1. A visual motion analysis method for analyzing an image sequence depicting a three-dimensional event including a plurality of objects moving relative to a background scene, the image sequence being recorded in a series of frames, each frame including image data forming a two-dimensional representation including a plurality of image regions depicting the moving objects and the background scene at an associated point in time, the method comprising:
- identifying a first moving object of the plurality of moving objects by comparing a plurality of frames of the image sequence and identifying a first image region of the image sequence including the first moving object, wherein the first image region includes a central portion surrounded by an outer edge; and
generating a layered global model including a background layer and a foreground component, wherein the foreground component includes exclusive spatial support region including image data located in the central portion of the first image region, and a probabilistic boundary region surrounding the exclusive spatial support region and including image data associated with the outer edge of the first image region.
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Abstract
A visual motion analysis method that uses multiple layered global motion models to both detect and reliably track an arbitrary number of moving objects appearing in image sequences. Each global model includes a background layer and one or more foreground “polybones”, each foreground polybone including a parametric shape model, an appearance model, and a motion model describing an associated moving object. Each polybone includes an exclusive spatial support region and a probabilistic boundary region, and is assigned an explicit depth ordering. Multiple global models having different numbers of layers, depth orderings, motions, etc., corresponding to detected objects are generated, refined using, for example, an EM algorithm, and then ranked/compared. Initial guesses for the model parameters are drawn from a proposal distribution over the set of potential (likely) models. Bayesian model selection is used to compare/rank the different models, and models having relatively high posterior probability are retained for subsequent analysis.
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Citations
46 Claims
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1. A visual motion analysis method for analyzing an image sequence depicting a three-dimensional event including a plurality of objects moving relative to a background scene, the image sequence being recorded in a series of frames, each frame including image data forming a two-dimensional representation including a plurality of image regions depicting the moving objects and the background scene at an associated point in time, the method comprising:
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identifying a first moving object of the plurality of moving objects by comparing a plurality of frames of the image sequence and identifying a first image region of the image sequence including the first moving object, wherein the first image region includes a central portion surrounded by an outer edge; and
generating a layered global model including a background layer and a foreground component, wherein the foreground component includes exclusive spatial support region including image data located in the central portion of the first image region, and a probabilistic boundary region surrounding the exclusive spatial support region and including image data associated with the outer edge of the first image region. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A visual motion analysis method for analyzing an image sequence depicting a three-dimensional event including a plurality of objects moving relative to a background scene, the image sequence being recorded in a series of frames, each frame including image data forming a two-dimensional representation including a plurality of moving image regions, each moving image region depicting one of the moving objects at an associated point in time, each frame also including image data associated with the background scene at the associated point in time, the method comprising:
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generating a plurality of layered global models utilizing image data from the image sequence, each layered global model including a background layer and at least one foreground component, wherein each foreground component includes exclusive spatial support region including image data from a central portion of an associated moving image region, and a probabilistic boundary region surrounding the exclusive spatial support region and including image data including an outer edge of the associated moving image region;
refining each foreground component of each layered global model such the exclusive spatial support region of each foreground component is optimized to the image data of the moving image region associated with said each foreground component; and
ranking the plurality of layered global models and identifying a layered global model that most accurately models the image data of the image sequence. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
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31. A visual motion analysis method for analyzing an image sequence depicting a three-dimensional event including a plurality of objects moving relative to a background scene, the image sequence being recorded in a series of frames, each frame including image data forming a two-dimensional representation including a plurality of moving image regions, each moving image region depicting one of the moving objects at an associated point in time, each frame also including image data associated with the background scene at the associated point in time, the method comprising:
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generating a plurality of layered global models utilizing image data from the image sequence, each layered global model including a background layer and at least one foreground component, wherein each foreground component includes exclusive spatial support region including image data from a central portion of an associated moving image region, and a probabilistic boundary region surrounding the exclusive spatial support region and including image data including an outer edge of the associated moving image region;
ranking the plurality of layered global models such that layered global models that relatively accurately model the image data of the image sequence are ranked relatively high, and layered global models that relatively inaccurately model the image data of the image sequence are ranked relatively low; and
eliminating low ranking global models from the plurality of layered global models. - View Dependent Claims (32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46)
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Specification