Sparse selective kernelized correlation filter model for visual object tracking

Xiaohuan Lu, Di Yuan, Zhenyu He, Donghao Li

Abstract: Robust visual object tracking is one of the most challenging issues in the field of computer vision. Because of the circular shifts strategy, correlation filter-based trackers show a great efficiency in tracking task and thus receive lots of attentions. However, most of the correlation filter-based trackers fix the scale of the targets in each frame and use single template to update the filters, which makes the trackers unreliable in the tracking task. In this paper, we intend to promote the robustness of the kernelized correlation filters (KCF) in the tracking task, through a fast scale pyramid solution to solve the scale variations problems. Furthermore, we introduce a sparse model selection scheme on template sets to solve the problem of contaminated templates in single template methods. We test our method on OTB-2013 dataset and the experimental results show the robustness of our method. The proposed tracker achieves promising performance both in terms of accuracy and speed comparing with the state-of-the-art trackers.