Computer Vision and Image Understanding
Computer Vision and Image Understanding
Accurate object contour tracking based on boundary edge selection
Pattern Recognition
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Moving object tracking in H.264/AVC bitstream
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Contour tracking using modified canny edge maps with level-of-detail
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
A new snake model robust on overlap and bias problems in tracking a moving target
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Object tracking and elimination using level-of-detail canny edge maps
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
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We propose a novel technique for tracking the visible boundary of a video object in the presence of occlusion. Starting with an initial contour that is interactively specified by the user and may be automatically refined by using intra-energy terms, the proposed technique employs piecewise contour prediction using local motion and color information on both sides of the contour segment, and contour snapping using scale-invariant intra-frame and inter-frame energy terms. The piecewise (segmented) nature of the contour prediction scheme and modeling of the motion on both sides of each contour segment enable accurate determination of whether and where the tracked boundary is occluded by another object. The proposed snake energy terms are associated with contour segments (as opposed to node points) and they are scale/resolution independent to allow multi-resolution contour tracking without the need to retune the weights of the energy terms at each resolution level. This facilitates contour prediction at coarse resolution and snapping at fine resolution with high accuracy. Experimental results are provided to illustrate the performance of the proposed occlusion detection algorithm and the novel snake energy terms that enable visible boundary tracking in the presence of occlusion.