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

D.A.G. Jauregui, P. Horain, M.K. Rajagopal, S.S.K. Karri
Inst. TELECOM, TELECOM SudParis, Evry, France
IEEE International Workshop on Multimedia Signal Processing (MMSP), 2010

@conference{jauregui2010real,

   title={Real-time particle filtering with heuristics for 3D motion capture by monocular vision},

   author={Jauregui, D.A.G. and Horain, P. and Rajagopal, M.K. and Karri, S.S.K.},

   booktitle={Multimedia Signal Processing (MMSP), 2010 IEEE International Workshop on},

   pages={139–144},

   organization={IEEE}

}

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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.
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