Target Tracking Algorithm Based on Deep Learning and Multi-Video Monitoring

In recent years, China's urban monitoring network has developed rapidly, and it has become increasingly intelligent and high-definition. Therefore, computer technologies are needed to solve some problems existed in target tracking currently.The image of urban monitoring is very complex with a wide variety of monitoring objects and a large number of objects, and frequent occlusions will exert between different objects When objects are moving, which can interfere with the accuracy of the recognition algorithm. In this paper,the tracking algorithm based on deep learning is studied intensively,and the MDNet (multi-domain Network) deep learning tracking model is combined with the modified Faster R-CNN target detection network, to improve the robustness and accuracy of the multi-target recognition method in the dynamic environment. The vot2015 data set is selected to test algorithm in this paper. The algorithm is compared with CF2 tracking algorithm which is based on deep learning in various environments,and The results show that the proposed algorithm improves the recognition accuracy and real-time recognition.