Tracking With Static and Dynamic Structured Correlation Filters

Tracking methods based on correlation filters have recently attracted attention for achieving fast tracking. However, their performance is somewhat limited in long-term tracking tasks, especially in an occlusion situation. To address this issue, we propose a novel structured correlation filter, which depends on coupled interactions between a static model and a dynamic model. Specifically, the static model exploits the star graph to capture spatial information and provides an initial estimation. The dynamic model based on Bayesian inference uses the rough location as a reference to estimate the final target state. Then, the dynamic model provides a feedback to the static regarding their updates. Finally, the dynamic model provides a scale adaptivity mechanism, which makes the proposed tracker effectively deal with not only partial occlusion but also scale variation. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed method performs favorably against the state-of-the-art tracking algorithms.