Abstract: In view of the image blurring caused by fast target movement, it is difficult for the DSST algorithm to distinguish between the target and the background information. The filter is cyclically shifted during the training phase to collect dense samples, which easily results in boundary effect and leads to the tracking drift problem. Therefore, this paper proposes an improved DSST real-time target trac- king algorithm (TLD-DSST) that incorporates the TLD framework. The algorithm improves the position filter of the DSST algorithm, adds the weight coefficient matrix through the spatial regularization me- thod to reduce the response of the non-target area, and performs rough positioning of the target under fast motion. At the same time, a naive Bayesian classifier is introduced to improve the TLD detector, in order to improve the detector's ability to distinguish between the target and the background information. Moreover, the optimal similarity matching is performed on the position of the DSST target response and the target area obtained by the TLD detector, so as to get the precise positioning result. The TLD detector positive and negative sample online update mechanism is used to continuously optimize the robustness of the algorithm. Experimental results show that the TLD-DSST algorithm has high accuracy and success rate for target tracking in complex scenarios such as fast motion.

Key words:  , TLD detector, boundary effect, spatial regularization, optimal similarity matching, naive Bayes classifier