Maneuvering head motion tracking by coarse-to-fine particle filter

Authors:
Yun-Qian Miao;Paul Fieguth;Mohamed S. Kamel
Affiliations:
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada;Department of System Design Engineering, University of Waterloo, Waterloo, Ontario, Canada;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
Venue:
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Year:
2011

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Abstract

Tracking a very actively maneuvering object is challenging due to the lack of state transition dynamics to describe the system's evolution. In this paper, a coarse-to-fine particle filter algorithm is proposed for such tracking, whereby one loop of the traditional particle filtering approach is divided into two stages. In the coarse stage, the particles adopt a uniform distribution which is parameterized by the limited motion range within each time step. In the following fine stage, the particles are resampled using the results of the coarse stage as the proposal distribution, which incorporates the most present observation. The weighting scheme is implemented using a partitioned color cue that implicitly embeds geometric information to enhance robustness. The system is tested by a publicly available dataset for tracking an intentionally erratic moving human head. The results demonstrate that the proposed system is capable of handling random motion dynamics with a relatively small number of particles.