Less false detections, fewer identity switches: methods for the improvement of deep sort

Deep sort algorithm is a multi-object tracking algorithm with high tracking accuracy and speed. However, due to the lack of detection filter and the association stage of a single frame, the accuracy of multi-object tracking is remaining enhancement. In this paper, we propose a DO-Adaptive NMS algorithm to filter the detections, and combine the K nearest neighbor algorithm with the intersection of union algorithm to sharpen features of the trajectories. Besides, we put forward a weighted algorithm of motion information and appearance information, which takes the disappear time of trajectories into consideration. Experiments show that the methods mentioned above all perform better than the original algorithm.