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This paper presents an efficient method for monocular recovering and tracking 3D human pose using 3D to 2D joints correspondences. Different from previous work, its main novelty lies in several aspects: Firstly, our method does not involve any complex features, which means that it does not tend to rely on good foreground segmentation. Secondly, formulating the model as an second order cone programming...
The objective of this paper is to parse object trajectories in surveillance video against occlusion, interruption, and background clutter. We present a spatio-temporal graph (ST-Graph) representation and a cluster sampling algorithm via deferred inference. An object trajectory in the ST-Graph is represented by a bundle of ldquomotion primitivesrdquo, each of which consists of a small number of matched...
This paper presents a novel approach for marker-less 3D full body pose tracking using adaptive particle filter. Firstly, the search space decomposition strategy and body part segmentation method are used to reduce the calculation complexity due to the large degrees of freedom. Then an adaptive particle filter is adopted to track each body part. This new technique is a significant improvement over...
This paper proposes a system for automatic segmentation and motion tracking of time-varying-meshes (TVM). Our approach is based on skeleton-based hierarchical mesh decomposition by distance calculation. The properties of the human skeleton structure are used to define the decomposition of each TVM frame. The proposed framework is a recursive system that iterates between automatic hierarchical decomposition...
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