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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...
We present a method for segmentation of articulated 3D shapes by incorporating the motion information obtained from time-varying models. We assume that the articulated shape is given in the form of a mesh sequence with fixed connectivity so that the inter-frame vertex correspondences, hence the vertex movements, are known a priori. We use different postures of an articulated shape in multiple frames...
For the complexity of the body movements in the videos of the athletic sports, the new video-based capture method is proposed, and it overcomes the errors when the body parts are obscured and the body joints do not meet the physical restraint system of the body. Compared with the other body motion capture techniques, it is low in cost, targeted and easy to implement. In terms of the design and modification...
This paper proposes a Euclidean-geodesic shape distribution for the more accurate retrieval of time-varying meshes, which are 3D mesh sequences of real-world objects generated by multiple cameras. The Euclidean-geodesic shape distribution derives from a combination of the modified shape distribution algorithm, which analyzes the global shape features of 3D models, and the geodesic shape distribution...
Today human motion understanding has become one of the most active research areas in computer vision. This is due to promising applications in areas such as visual surveillance, human performance analysis, computer-human interfaces (robotic interaction with humans), content-based video retrieval/storage and virtual reality. This work is focused on recognizing human actions. A novel motion analysis...
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