title: Efficient object tracking by incremental self-tuning particle filtering on the affine group.
abstract: We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group, which can find the optimal state in a chainlike way with a very small number of particles. Unlike traditional particle filtering, which only relies on random sampling for state optimization, ISPF incrementally draws particles and utilizes an online-learned pose estimator (PE) to iteratively tune them to their neighboring best states according to some feedback appearance-similarity scores. Sampling is terminated if the maximum similarity of all tuned particles satisfies a target-patch similarity distribution modeled online or if the permitted maximum number of particles is reached. With the help of the learned PE and some appearance-similarity feedback scores, particles in ISPF become "smart" and can automatically move toward the correct directions; thus, sparse sampling is possible. The optimal state can be efficiently found in a step-by-step way in which some particles serve as bridge nodes to help others to reach the optimal state. In addition to the single-target scenario, the "smart" particle idea is also extended into a multitarget tracking problem. Experimental results demonstrate that our ISPF can achieve great robustness and very high accuracy with only a very small number of particles.
authors.affiliation: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China. mli@nlpr.ia.ac.cn
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.forename: Tieniu
authors.initials: T
authors.lastname: Tan
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Kaiqi
authors.initials: K
authors.lastname: Huang