Abstract
Moving target tracking is widely used in different fields. In practical applications, it faces the complex situations such as mutual occlusions among targets and rapid movement of targets, and needs to solve the problems of tracking accuracy and real time for the algorithms. To address the problems, the paper takes the moving targets of ships, yachts, and aircraft carriers at sea as the research objects, studies the maritime moving target tracking models under big data and small sample data environments, presents a tracking algorithm combining motion state estimation and convolutional neural network (CNN). Integrating Edge Boxes with CNN to realize multi-target detection at sea, the recall rate and accuracy of target detection are improved under the condition of higher detection efficiency. The target recommendation areas are generated based on the motion state estimation, and continuous information between video frames is used to improve estimation accuracy and effectively deal with occlusions among targets through efficient prediction of target states. The training model is utilized to detect and track the targets in the recommended areas. Compared with the tracking method of Fast R-CNN, as well as the tracking method based on histogram of oriented gradient (HOG) and support vector machine (SVM), the experiment results show the proposed algorithm has higher tracking accuracy and better real time even in the scenes of mutual occlusions among targets and rapid movement of targets.
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This paper was supported by National Natural Science Fund of China (Grant No. 61371143).
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Zhang, Y., Ma, J., Guo, Q., Lv, W. (2020). A Moving Target Tracking Algorithm Based on Motion State Estimation and Convolutional Neural Network. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_35
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