Abstract
Generally, particle filters need a large number of particles to approximate the posterior for the purpose of ideal effect. Previous methods extract remarkable particles from the particles at time t-1 by nonlinear function. Those methods use the remarkable particles to reduce the number of particles and improve the accuracy of particle filter. However, the nonlinear function extracts the remarkable particles, which will weaken or even ignore useful remarkable local particles. Thus this paper presents a new resampling scheme to extract remarkable local particles. We propose a weight threshold and a distance threshold to extract remarkable local particles from particles at time t-1. Meanwhile, we use these remarkable local particles to track the target analytically. Besides, we propose a global transition model to improve the accuracy of the particle filter. Based on remarkable local resampling scheme and the global transition model, we propose a new framework of particle filter. Finally, experiments show that our framework has higher efficiency than previous methods in the case of fewer particles.
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Acknowledgments
Thank the editors and the anonymous referees for their valuable comments. This work was supported by the National Nature Science of China (No. 61572214 and 61462048), and Wuhan Science and Technology Bureau of Hubei Province, China (No. 2014010202010110).
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Zhao, Z., Wang, T., Liu, F. et al. Remarkable local resampling based on particle filter for visual tracking. Multimed Tools Appl 76, 835–860 (2017). https://doi.org/10.1007/s11042-015-3075-6
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DOI: https://doi.org/10.1007/s11042-015-3075-6