The PARAChute project: remote monitoring of posture and gait for fall prevention
EURASIP Journal on Applied Signal Processing
3D Human Motion Tracking Using Progressive Particle Filter
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Reducing particle filtering complexity for 3D motion capture using dynamic Bayesian networks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
3D human motion tracking based on a progressive particle filter
Pattern Recognition
Tracking in object action space
Computer Vision and Image Understanding
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In this paper we present a new approach for marker less human motion capture from conventional camera feeds. The aim of our study is to recover 3D positions of key points of the body that can serve for gait analysis. Our approach is based on foreground segmentation, an articulated body model and particle filters. In order to be generic and simple no restrictive dynamic modelling was used. A new modified particle filtering algorithm was introduced. It is used efficiently to search the model configuration space. This new algorithm which we call Interval Particle Filtering reorganizes the configurations search space in an optimal deterministic way and proved to be efficient in tracking natural human movement. Results for human motion capture from a single camera are presented and compared to results obtained from a marker based system. The system proved to be able to track motion successfully even in partial occlusions.