Towards model-based recognition of human movements in image sequences
CVGIP: Image Understanding
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
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Computer Vision and Image Understanding
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computer Vision and Image Understanding
Human Motion Parameter Capturing Using Particle Filter and Nonparametric Belief Propagation
SSIAI '08 Proceedings of the 2008 IEEE Southwest Symposium on Image Analysis and Interpretation
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Image and Vision Computing
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This paper proposes a novel human motion capture method that locates human body joint position and reconstructs the human pose in 3D space from monocular images.We propose a two-stage framework including 2D and 3D probabilistic graphical models which can solve the occlusion problem for the estimation of human joint positions. The 2D and 3D models adopt directed acyclic structure to avoid error propagation of inference. Image observations corresponding to shape and appearance features of humans are considered as evidence for the inference of 2D joint positions in the 2D model. Both the 2D and 3D models utilize the Expectation Maximization algorithm to learn prior distributions of the models. An annealed Gibbs sampling method is proposed for the two-stage method to inference the maximum posteriori distributions of joint positions. The annealing process can efficiently explore the mode of distributions and find solutions in high-dimensional space. Experiments are conducted on the HumanEva dataset with image sequences of walking motion, which has challenges of occlusion and loss of image observations. Experimental results show that the proposed two-stage approach can efficiently estimate more accurate human poses.