Online Multi-object Tracking Based on Deep Learning

Multi-object tracking task aims to identify and track all targets in the video. It has important applications in intelligent monitoring and other fields. Two problems can affect the accuracy of the multi-object tracking task. First, occlusion between targets will lead to interruption of tracking trajectory and switch of tracking target. Second, quality of the object detection results will directly affect the tracking accuracy. In this paper, we adopt a single-object tracking algorithm based on deep learning is introduced to solve the first problem and develop a discriminant network scoring the accuracy of detection and prediction bounding boxes to solve the second problem. The experimental results show that the proposed tracker performs better than other competing methods.