A Structural Correlation Filter Combined with A Multi-task Gaussian Particle Filter for Visual Tracking
This work addresses visual tracking challenges like fast motions and occlusions for computer vision applications, representing an incremental improvement through hybrid method integration.
The paper tackles robust visual tracking by proposing a KCF-GPF model that combines structural correlation filters with a multi-task Gaussian particle filter, achieving favorable performance against 16 state-of-the-art trackers on the OTB-2013 dataset with 50 sequences.
In this paper, we propose a novel structural correlation filter combined with a multi-task Gaussian particle filter (KCF-GPF) model for robust visual tracking. We first present an assemble structure where several KCF trackers as weak experts provide a preliminary decision for a Gaussian particle filter to make a final decision. The proposed method is designed to exploit and complement the strength of a KCF and a Gaussian particle filter. Compared with the existing tracking methods based on correlation filters or particle filters, the proposed tracker has several advantages. First, it can detect the tracked target in a large-scale search scope via weak KCF trackers and evaluate the reliability of weak trackers\rq decisions for a Gaussian particle filter to make a strong decision, and hence it can tackle fast motions, appearance variations, occlusions and re-detections. Second, it can effectively handle large-scale variations via a Gaussian particle filter. Third, it can be amenable to fully parallel implementation using importance sampling without resampling, thereby it is convenient for VLSI implementation and can lower the computational costs. Extensive experiments on the OTB-2013 dataset containing 50 challenging sequences demonstrate that the proposed algorithm performs favourably against 16 state-of-the-art trackers.