DocumentCode :
2611058
Title :
Tracking 3D Human Body using Particle Filter in Moving Monocular Camera
Author :
Kim, Sungmin ; Park, Chang-Beom ; Lee, Seong-Whan
Author_Institution :
Center for Artificial Vision Res., Korea Univ., Seoul
Volume :
4
fYear :
0
fDate :
0-0 0
Firstpage :
805
Lastpage :
808
Abstract :
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
Keywords :
Monte Carlo methods; computer vision; feature extraction; image matching; image motion analysis; image sampling; object detection; particle filtering (numerical methods); statistical distributions; stereo image processing; target tracking; trees (mathematics); video signal processing; 3D human body tracking; Monte Carlo sampling; articulation model; boundary distance; hierarchical tree structure; human motion; human movement; image sequence; moving monocular camera; particle filter; posterior distribution; random movement; random probability; similarity matching; unconstrained movement; video sequence; Biological system modeling; Cameras; Humans; Image sequences; Particle filters; Particle tracking; Predictive models; Shape; Tree data structures; Video sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
ISSN :
1051-4651
Print_ISBN :
0-7695-2521-0
Type :
conf
DOI :
10.1109/ICPR.2006.1130
Filename :
1699962
Link To Document :
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