Tracking segmented objects using tensor voting

The paper presents a new approach to track objects in motion when observed by a fixed camera, with severe occlusions, merging/splitting objects and defects in the detection. We first detect regions corresponding to moving objects in each frame, then try to establish their trajectory. We propose to i...

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Bibliographic Details
Main Authors: Kornprobst, P., Medioni, G.
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Computer vision
Electrical capacitance tomography
Image restoration
Iris
Merging
Motion detection
Tensile stress
Tracking
Voting
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Summary:The paper presents a new approach to track objects in motion when observed by a fixed camera, with severe occlusions, merging/splitting objects and defects in the detection. We first detect regions corresponding to moving objects in each frame, then try to establish their trajectory. We propose to implement the temporal continuity constraint efficiently, and apply it to tracking problems in realistic scenarios. The method is based on a spatiotemporal (2D+t) representation of the moving regions, and uses the tensor voting methodology to enforce smoothness in space and table of the tracked objects. Although other characteristics may be considered, only the connected components of the moving regions are used, without further assumptions about the object being tracked. We demonstrate the performance of the system on several real sequences.
ISSN:1063-6919
DOI:10.1109/CVPR.2000.854756