Abstract: Aiming at the problem that the current multiple object tracking algorithm had poor tracking effect in the face of frequent target occlusion, using Mask R-CNN as a detector, according to the detection results, the Kalman filter was used to predict the position of the tracking target in the next frame image and the improved Hungarian algorithm was used for data association, and the trajectory correction scheme was used to deal with the problem of trajectory interruption. The algorithm was experimented on each test set of MOT16 dataset, the experimental results show that the tracking accuracy of the algorithm is 55.1%, and the effect of target occlusion is better.

Key words: machine vision, target tracking, Hungarian algorithm, deep learning