Anti-Occlusion Moving Target Tracking Algorithm Based on Multifeature Self-Adaptive Fusion

In view of the current target tracking algorithm, it is difficult to effectively track the target when it is blurred or occluded; therefore, an anti-occlusion algorithm based on multi feature adaptive fusion is proposed in this study. First, the gradient direction histogram feature HOG and color histogram feature are extracted, and the correlation filtering response of the two features is adaptively fused to maximize the tracking quality. In the tracking process, a high-quality filter template is stored according to the quality of the response map, and the quality difference between the high-quality template and normal update template is used to detect the occlusion of the target. When the target occlusion disappears, the template of the tracker traces back to the high-quality template to retrack the target. According to the experimental results for OTB100 and UAV123, this algorithm has a better performance than other similar correlation filtering algorithms and can still track well in the case of target occlusion.