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Real-time particle filtering with heuristics for 3D motion capture by monocular vision

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4 Author(s)
Jauregui, D.A.G. ; Inst. TELECOM, TELECOM SudParis, Evry, France ; Horain, P. ; Rajagopal, M.K. ; Karri, S.S.K.

Particle filtering is known as a robust approach for motion tracking by vision, at the cost of heavy computation in a high dimensional pose space. In this work, we describe a number of heuristics that we demonstrate to jointly improve robustness and real-time for motion capture. 3D human motion capture by monocular vision without markers can be achieved in realtime by registering a 3D articulated model on a video. First, we search the high-dimensional space of 3D poses by generating new hypotheses (or particles) with equivalent 2D projection by kinematic flipping. Second, we use a semi-deterministic particle prediction based on local optimization. Third, we deterministi-cally resample the probability distribution for a more efficient selection of particles. Particles (or poses) are evaluated using a match cost function and penalized with a Gaussian probability pose distribution learned off-line. In order to achieve real-time, measurement step is parallelized on GPU using the OpenCL API. We present experimental results demonstrating robust real-time 3D motion capture with a consumer computer and webcam.

Published in:

Multimedia Signal Processing (MMSP), 2010 IEEE International Workshop on

Date of Conference:

4-6 Oct. 2010