Robust Object Tracking Via Part-Based Correlation Particle Filter

In this paper, a part-based correlation particle filter framework is proposed for robust visual tracking. Through managing target parts by correlation filters in a particle filter framework, we comprehensively model the target appearance using plentiful overlapped local parts with different positions and sizes. Further, we propose a particle re-sampling mechanism with appearance and geometry reliability consideration to resam-ple the redundant particles, which guides our tracker to focus more on the discriminative and reliable local parts. Finally, to cope with the limited search range of local tracker and model corruption caused by unreliable samples, we introduce the top-down coarse-to-fine localization and bottom-up adaptive update strategies to further boost the performance. Extensive experimental results on three challenging datasets demonstrate that our tracking algorithm performs favorably against state-of-the-art methods. Specifically, our approach exhibits superior performance on tracking nonrigid objects with rotation and large deformation.