Attenuated Sequential Importance Resampling (A-SIR) Algorithm for Object Tracking
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.
KeywordsRoot Mean Square Error Object Tracking Importance Weight Visual Tracking Proposal Distribution
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- 2.Douc, R., Cappe, O.: Comparison of resampling schemes for particle filtering. In: 4th International Symposium on in Image and Signal Processing and Analysis, pp. 64–69 (2005)Google Scholar
- 3.Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
- 4.Yunqiang, C., Yong, R.: Real time object tracking in video sequences. In: Signals and Communications Technologies, Interactive Video, vol. II, pp. 67–88. Springer, Heidelberg (2006)Google Scholar
- 8.Wu, G., Tang, Z.: A new resampling strategy about particle filter algorithm applied in Monte Carlo framework. In: Second International Conference on Intelligent Computation Technology and Automation, pp. 507–510 (2009)Google Scholar
- 9.Wang, F., Lin, Y.: Improving Particle Filter with A New Sampling Strategy. In: 4th International Conference on Computer Science and Education, pp. 408–412 (2009)Google Scholar