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Published in:
2010 | OriginalPaper | Chapter
Depth Assisted Occlusion Handling in Video Object Tracking
Authors : Yingdong Ma, Qian Chen
Published in: Advances in Visual Computing
Publisher: Springer Berlin Heidelberg
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We propose a depth assisted video object tracking algorithm that utilizes a stereo vision technique to detect and handle various types of occlusions. The foreground objects are detected by using a depth and motion-based segmentation method. The occlusion detection is achieved by combining the depth segmentation results with the previous occlusion status of each track. According to the occlusion analysis results, different object correspondence algorithms are employed to track objects under various occlusions. The silhouette-based local best matching method deals with severe and complete occlusions without assumptions of constant movement and limited maximum duration. Experimental results demonstrate that the proposed system can accurately track multiple objects in complex scenes and provides improvements on dealing with different partial and severe occlusion situations.