In the traditional correlation filter object tracking algorithm,due to the limitation of the cosine window and the search area,a tracking drift can easily occur in complex scenes.A framework is proposed in the context-aware algorithm to allow global contextual information to be incorporated into the correlation filter tracker,but the same suppression weight is directly used instead of calculating the interference degree of the context information to the target and it cannot adapt to giving background information with different degrees of interference,on the basis of which we propose a context suppression weight adaptive correlation filter target tracking algorithm.First,the background information arpond the target is learned into the filter,enhancing the classification ability of the filter template for target and context background information,and the adaptive weight coefficient vector is introduced.Second,a formula for the context information interference coefficient is proposed to quantitatively evaluate the interference degree of the context information to the target.Third,according to the proposed formula,the interference degree of context information is calculated,and then it is matched with the adaptive weight coefficient vector,so as to lead to the effect that,the greater the interference degree of the context information to the target,the greater the suppression degree.Finally,we rely on the OTB100 dataset to verify the effectiveness of the algorithm in this paper,with experimental results showing that the success rate and accuracy of the algorithm in this paper are improved by 5.7% and 4.3%,respectively compared with the benchmark algorithm,and that it has strong robustness.
