Structural similarity-based object tracking in multimodality surveillance videos
Machine Vision and Applications
A New Resampling Strategy about Particle Filter Algorithm Applied in Monte Carlo Framework
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 01
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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This paper presents a newly developed attenuating resampling algorithm for particle filtering that can be applied to object tracking. In any filtering algorithm adopting concept of particles, especially in visual tracking, re-sampling is a vital process that determines the algorithm's performance and accuracy in the implementation step.It is usually a linear function of the weight of the particles, which decide the number of particles copied. If we use many particles to prevent sample impoverishment, however, the system becomes computationally too expensive. For better real-time performance with high accuracy, we introduce a steep Attenuated Sequential Importance Re-sample (A-SIR) algorithm that can require fewer highly weighted particles by introducing a nonlinear function into the resampling method. Using our proposed algorithm, we have obtained very impressive results for visual tracking with only a few particles instead of many. Dynamic parameter setting increases the steepness of resampling and reduces computational time without degrading performance. Since resampling is not dependent on any particular application, the A-SIR analysis is appropriate for any type of particle filtering algorithm that adopts a resampling procedure. We show that the A-SIR algorithm can improve the performance of a complex visual tracking algorithm using only a few particles compared with a traditional SIR-based particle filter.