A tracking method combines with detection algorithm based on deep separable convolution network

Abstract

With the emergence of a large number of artificial intelligence technologies, deep learning has become the key technology in computer vision area. Object tracking is one of the most important technology in the field of computer vision. Thus we studied about tracking algorithms and proposed a method mainly hopes to solve the occlusion problem in complex tracking scene. Using object detection algorithms based on deep learning to increase the speed of associations and improve tracking effect. It can return the position of the tracking object unsupervised. Then extract features to store in features library, so that the prediction of trajectory whose features can highly be matched is more accurate and the associations are more reliable. Experiment shows our tracking algorithm combines with detection algorithm based on depthwise separable convolution networks not only has a smaller and faster model but also achieved a robustness and real-time tracking in scene where objects are under occlusions.