Effect of resampling steepness on particle filtering performance in visual tracking

This paper presents a proficiently developed resampling Algorithm for particle filtering. In any filtering Algorithm adopting the perception of particles, especially in visual tracking, resampling is an essential 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 determines 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 Sequential Importance Resampling (S-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 remarkable results for visual tracking with only a few particles instead of many. Dynamic parameter setting boosts the steepness of resampling and reduces computational time without degrading performance. Since resampling is not dependent on any particular application, the S-SIR analysis is appropriate for any type of particle filtering Algorithm that adopts a resampling procedure. We show that the S-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.