Variance reduction techniques in particle-based visual contour tracking
Pattern Recognition
Self-localization and stream field based partially observable moving object tracking
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing advances in robots and autonomy
Online selection of tracking features using AdaBoost
IEEE Transactions on Circuits and Systems for Video Technology
Exploiting motion correlations in 3-D articulated human motion tracking
IEEE Transactions on Image Processing
Adaptive pyramid mean shift for global real-time visual tracking
Image and Vision Computing
Computers & Mathematics with Applications
An Efficient Particle Filter---based Tracking Method Using Graphics Processing Unit (GPU)
Journal of Signal Processing Systems
An adaptive sample count particle filter
Computer Vision and Image Understanding
State estimation of a supply chain using improved resampling rules for particle filtering
Proceedings of the Winter Simulation Conference
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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