Target Tracking Algorithm Based on Density Clustering

The traditional Siamese network-based target tracking algorithm needs to use the convolution feature of the target to scan around the target location when predicting the location of the target in the next frame image, and perform similarity calculation to obtain the similarity score matrix with the highest score. It is the next frame target position. The highest similarity score often does not represent the precise target position of the target, which is often affected by the sliding step size during scanning. Aiming at this problem, this paper proposes a target tracking method based on density clustering. By combining the Siamese network to predict the next frame target position, and adding the target’s motion trajectory information, the direction of the target motion is given more weight, the other directions are given a smaller weight, and finally the target position is predicted by the density clustering method. The results show that the proposed algorithm effectively improves the accuracy of the target location prediction of the Siamese network when tracking targets.