Description: In this paper , a novel approach is proposed for t racking markerless human motion in monocular
videos to capture the articulate motion data1 With an articulated human model const ructed , the new ap2
proach uses the probability density propagation of the particle filters through the learnt motion model and
likelihood computing with the appearance models to t rack the human motion1 The method is capable of auto2
matically recovering f rom t racking failures1 It can also process the occlusion and auto2occlusion problem cor2
rectly1 Experimental result s f rom real monocular videos show that the new approach is robust and the t rack2
ing result s are satisfactory1
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单目视频中无标记的人体运动跟踪-陈坚粒子滤波.pdf
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