A Target Tracking Method Based on KCF for Omnidirectional Vision
Chengtao Cai, Xin Liang, Boyu Wang, Yujia Cui, Yongjie Yan · 2018
Omnidirectional vision can solve the problem that the tracking object easily lost in perspective vision with narrow sight.In this paper, we propose a tracking method based on Kernel Correlation Filtering(KCF) for omnidirectional vision.For the insufficiency of the KCF algorithm, we make some improvements about scale and occlusion.In our algorithm, we combine the multi-scale KCF with kalman estimate. In addition, occlusion judgement and re-detection are added to our algorithm. Due to the large filed-of-view and distortion of the omnidirectional, the target is re-detected using SURF in a local area around the predictive window. Experiments show that our method has better long-term tracking performance in the case of scale variation, occlusion for omnidirectional vision system.