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The challenge of markerless human motion tracking is the high dimensionalityof the search space. Thus, efficient exploration in the search space is ofgreat significance. In this paper, a motion capturing algorithm is proposed forupper body motion tracking. The proposed system tracks human motion based onmonocular silhouette-matching, and it is built on the top of a hierarchicalparticle filter, within which a novel deterministic resampling strategy (DRS)is applied. The proposed system is evaluated quantitatively with the groundtruth data measured by an inertial sensor system. In addition, we compare theDRS with the stratified resampling strategy (SRS). It is shown in experimentsthat DRS outperforms SRS with the same amount of particles. Moreover, a new 3Darticulated human upper body model with the name 3D cardbox model is createdand is proven to work successfully for motion tracking. Experiments show thatthe proposed system can robustly track upper body motion withoutself-occlusion. Motions towards the camera can also be well tracked.

Particle Filter Based Monocular Human Tracking with a 3D Cardbox Model and a Novel Deterministic Resampling Strategy