In this paper, we propose a novel correlation particle filter (CPF) for robust visual tracking. Instead of a simple combination of a correlation filter and a particle filter, we exploit and complement the strength of each one. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has four major advantages: 1) it is robust to partial and total occlusions, and can recover from lost tracks by maintaining multiple hypotheses; 2) it can effectively handle large-scale variation via a particle sampling strategy; 3) it can efficiently maintain multiple modes in the posterior density using fewer particles than conventional particle filters, resulting in low computational cost; and 4) it can shepherd the sampled particles toward the modes of the target state distribution using a mixture of correlation filters, resulting in robust tracking performance. Extensive experimental results on challenging benchmark data sets demonstrate that the proposed CPF tracking algorithm performs favorably against the state-of-the-art methods.
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61432019, Grant 61572498, Grant 61532009, Grant 61572493, and Grant U1536203, in part by the Beijing Natural Science Foundation under Grant 4172062, and in part by the Key Research Program of Frontier Sciences, CAS, under Grant QYZDJSSW-JSC039.
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design