Combination of accumulated motion and color segmentation for human activity analysis
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
3D human motion tracking based on a progressive particle filter
Pattern Recognition
Comparison of stochastic filtering methods for 3D tracking
Pattern Recognition
Estimation of the number of humans and their movement paths in a room using binary infrared sensors
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
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In this paper, we propose a method for human tracking using 3D human body model in a video sequence with a monocular moving camera. Tracking a human with unconstrained movement in moving monocular camera image sequence is extremely challenging. Our 3D human body model which is formed with articulation model of hierarchical tree structure can express all human's movement by parameters. We can obtain 3D human body model which has the most similar shape with input image through similarity matching. In order to predict the region and movement of human using 3D human body model in the obtained current frame, we use the particle filter which predicts the posterior distribution by the random probability variable based on Monte Carlo sampling. As a result, it can be possible to track robustly for human's motion and random movement of camera in the environment with moving camera. We can get the result of converging toward minimized error values using boundary distance between a predicted 3D human body model and an input image. In the result of experiment, the proposed method showed correct tracking result for complex background and various human movements.