Research On Correlation Filter And Siamese Network Hybrid Algorithm For Visual Object Tracking

Target tracking technology is widely used in security surveillance,autonomous driving,military guidance and other fields.The mainstream target tracking algorithms are divided into two categories:the tracking algorithm based on correlation filter and the tracking algorithm based on Siamese Network.Target tracking algorithms based on correlation filter have attracted attention due to their high-efficiency calculation speed and excellent tracking accuracy,but these are easy to track failure of scale variation,fast motion and other scenarios.Target tracking algorithms based on Siamese Network have better tracking accuracy and robustness due to the strong characterization ability of deep feature,but these are easy to track failure of deformation,background clutters and other scenarios.By analyzing the characteristics of target tracking algorithms based on correlation filter and target tracking algorithms based on Siamese Network,a hybrid visual object tracking based on correlation filter and Siamese Network is proposed.First,the video sequence is grouped by a fixed number of frames.Subsequently,in the group,KCF is used for fast target tracking,and in the last frame of each group,the target tracking algorithm based on Siamese Network is used for correction to improve the tracking performance degradation of KCF algorithm in the scale variation,occlusion and other situations,so as to avoid tracking failure caused by error accumulation.Furthermore,two improvement strategies are adopted to improve the tracking performance of the algorithm:(1)A linear feature template updating strategy is proposed,which makes the SiamFC algorithm introduce the change information of the target appearance feature and retain most target features in the first frame simultaneously,so as to deal with the complex and changeable situation of the video target effectively.(2)A multi-resolution segmenting preprocessing strategy is proposed to improve the tracking performance of KCF algorithm in low resolution video.Compared with the current advanced target tracking algorithm in OTB-2013 and OTB-100 datasets,the experimental results show that the proposed algorithm can effectively improve the tracking performance.On the OTB-2013 dataset,the accuracy of improved algorithm outperforms SiamFC and KCF by 4.1%and 7.5%respectively,the success rate of improved algorithm outperforms SiamFC and KCF by 4.3%and 10.0%respectively.On the OTB-100 dataset,the accuracy of improved algorithm outperforms SiamFC and KCF by 4.4%and 10.1%respectively,the success rate of improved algorithm outperforms SiamFC and KCF by 3.5%and 11.8%respectively.The improved algorithm performs favorably against the state-of-the-art trackers with challenging factors,especially for the video of illumination variation,scale variation,rotation,fast motion,motion blur,in-plane rotation,out-of-plane rotation,and out-of-view.