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Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance

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2 Author(s)
Xinyu Xu ; Dept. of Comput. Sci. & Eng., Arizona State Univ., Tempe, AZ ; Baoxin Li

Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter

Published in:

Image Processing, IEEE Transactions on  (Volume:16 ,  Issue: 3 )