自适应上下文感知相关滤波跟踪

Aiming at the problem of background information filtering too smooth when implementing equivalent weight training to context sample in context-aware correlation filter tracking algorithm, we propose an adaptive context-aware correlation filtering algorithm. And in order to solve the problem of target occlusion, we introduce a new occlusion criterion. First of all, extract background samples from the four directions of the target to learn in the filter. The target motion state is estimated by Kalman Filters and the direction of the target is predicted. During the training of the filter, more weight is given to the background sample training in the direction of the target movement. Then, a new occlusion indicator Average Peak-to correlation Energy(APCE) is introduced when the model is updated. The target model is updated only when the response peaks and APCE values are in proportional higher than their respective historical averages. Finally, the proposed algorithm is compared with some mainstream tracking algorithms in CVPR 2013 Benchmark. Simulation results show that the accuracy rate and success rate of the proposed algorithm respectively are 0.810 and 0.701, which are superior to other algorithms. The results fully reflect the robustness of the proposed algorithm.