A pedestrian tracking algorithm based on background unrelated head detection
Yibing Zhang, Tao Fan · 2017
Aiming at the problem that pedestrian tracking algorithm is prone to target tracking error in complex background, this paper proposes a pedestrian tracking algorithm based on human head detection to adapt to pedestrian tracking in many complex scenes. Firstly, the foreground segmentation technique is used to extract the motion foreground quickly. In the Adaboost classifier, the human body negative sample is added, and the Haar-like feature is used to detect the head on the basis of the movement foreground. The target tracking chain is established by detecting the head Walking tracker. The experimental results show that the algorithm proposed in this paper reduces the false detection rate and missed detection rate of the head, and improves the robustness to pedestrian tracking in many complex scenes.