Abstract: Aiming at the problem of slow speed of the convolutional neural network target tracking algorithm, a target tracking algorithm combining fast multi-domain convolutional neural network (Faster MDNet) and optical flow method is proposed. The optical flow method is used to obtain the moving state of the target, and the preliminary selection box is used as the tracking target position. Then, the preliminary selection box is used as the input of Faster MDNet, and Faster MDNet is used as the detector to obtain the exact position and bounding box of the tracking target. Experiments on the target tracking benchmark data set VOT2014 prove that the algorithm’s online tracking speed is increased by 8 times and the accuracy is improved by 10%.

Key words: deep learning, convolutional neural network, optical flow method, target tracking