SYSTEM AND METHOD FOR VISUAL MOTION BASED OBJECT SEGMENTATION AND TRACKING
First Claim
1. A detection and tracking method for detecting and tracking objects within a sequence of video image frames, the method comprising;
- detecting keypoints in a current image frame of the video image frames;
assigning local appearance features to the detected keypoints;
establishing Point-Motion-Pairs between the current image frame and a previous image frame of the video image frames, using the local appearance features;
using the Point-Motion-Pairs to form seed coherent motion object models; and
accumulating additional matches between image locations in the current image frame and the previous image frame to form complete coherent motion object models of the objects being tracked, wherein the steps are performed by at least one electronics processor.
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Abstract
The PMP Growth algorithm described herein provides for image tracking, segmentation and processing in environments where the camera system moves around a great deal, i.e., causing image jumps from one image frame to the next. It also is operative in systems where the objects themselves are making quick movements that alter their path. Attributes of the PMP Growth algorithm allow tracking systems using the PMP Growth algorithm to follow objects a long distance in a scene. This detection and tracking method is designed to track objects within a sequence of video image frames, and includes detecting keypoints in a current image frame of the video image frames, assigning local appearance features to the detected keypoints, establishing Point-Motion-Pairs between two successive image frames of the video image frames, and accumulating additional matches between image locations to form complete coherent motion object models of the objects being tracked. The segmentation aspect permits for the discovery of different coherently moving regions in the images.
101 Citations
20 Claims
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1. A detection and tracking method for detecting and tracking objects within a sequence of video image frames, the method comprising;
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detecting keypoints in a current image frame of the video image frames; assigning local appearance features to the detected keypoints; establishing Point-Motion-Pairs between the current image frame and a previous image frame of the video image frames, using the local appearance features; using the Point-Motion-Pairs to form seed coherent motion object models; and accumulating additional matches between image locations in the current image frame and the previous image frame to form complete coherent motion object models of the objects being tracked, wherein the steps are performed by at least one electronics processor. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for visual motion based object segmentation and tracking, the method comprising:
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inputting a current image frame; inputting state data carried over from a previous image frame; detecting keypoint locations in the current image frame; computing an appearance feature for each keypoint that can be compared with appearance features of keypoints and model points from the previous image frame; performing Delaunay Triangulation on the current image frame keypoints, and on the model points of each of the previous image frame Similarity Transform Objects Models (STOMs); generating a catalog of appearance correspondence matches between model points of the previous image frame and keypoint features of the current image frame; forming, by use of the appearance correspondence matches, Point-Motion-Pairs between the previous image frame STOMs'"'"' model keypoints and the current image frame keypoints; forming clusters of the Point-Motion-Pairs using compatible similarity transforms; choosing a cluster of the Point-Motion-Pairs to seed the current image frame with STOMs; growing the STOMs to include as many current image frame keypoints as possible; merging consistent STOMs in the current image frame together; finding consistent Point-Motion-Pair mappings between remaining image keypoints from the current image frame and keypoints that were left unexplained in the previous image frame to seed new STOMs; and outputting a set of STOMs representing coherent motion regions between the current image frame and the previous image frame, and the set of remaining unexplained keypoints of the current image frame, wherein the steps are performed using at least one electronic processor. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
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17. A system for detection and tracking objects within a sequence of video image frames, the system comprising:
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at least one an electronic processor device configured to; detect keypoints in a current image frame of the video image frames; assign local appearance features to the detected keypoints; establish Point-Motion-Pairs between the current image frame and a previous image frame of the video image frames, using the local appearance features; use the Point-Motion-Pairs to form seed coherent motion object models; and accumulate additional matches between image locations in the current image frame and the previous image frame to form complete coherent motion object models of the objects being tracked. - View Dependent Claims (18, 19, 20)
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Specification