深度学习的目标跟踪算法综述

Object tracking is a fundamental problem in computer vision, which uses context information in a video or image sequence to predict and locate a target (s). It is widely used in smart video monitoring systems, intelligent human interaction, intelligent transportation, visual navigation systems, and many other areas. With the advent of the big data era and the emergence of deep learning methods, tracking performance has substantially improved. In this paper, we introduce the basic research framework of object tracking and review the history of object tracking from the perspective of the observation model. We indicate that deep learning allows for a more robust observation model to be obtained. We review the deep learning methods that are suitable for object tracking from the aspects of deep discriminative model and deep generative model. We also classify and analyze the existing deep object tracking methods from the perspectives of network structure, network function, and network training. In addition, we introduce several other deep object tracking methods, including deep object tracking based on the fusion of classification and regression, on reinforcement learning, on ensemble learning, and on meta-learning. We show the current commonly used databases for object tracking based on deep learning and their evaluation methods. We likewise analyze and summarize the latest specific application scenarios in object tracking from the perspectives of mobile tracking system, detection, and tracking-based system. Finally, we analyze the problems of object tracking, including insufficient training data, real-time tracking, and long-term tracking and specify further research directions for deep object tracking.