Survey of Deep Online Multi-object Tracking Algorithms

Abstract

Video multi-object tracking is a key task in the field of computer vision and has a wide application prospect in industry, commerce and military fields. At present, the rapid development of deep learning provides many solutions to solve the problem of multi-object tracking. However, the challenging problems such as mutation of target appearance, serious occlusion of target area, disappearance and appearance of target have not been completely solved. This paper focuses on online multi-object tracking algorithm based on deep learning, and summarizes the latest progress in this field. According to the three important modules of feature prediction, apparent feature extraction and data association, as will as the two frameworks of detection-based-tracking (DBT) and joint-detection-tracking (JDT), this paper divides deep online multi-object tracking algorithms into six sub-classes, and discusses the principles, advantages and disadvantages of different types of algorithms. Among them, the multi-stage design of the DBT algorithm has a clear structure and is easy to optimize, but multi-stage training may lead to sub-optimal solutions; the sub-modules of the JDT algorithm that integrates detection and tracking achieve faster inference speed, but there is a problem of collaborative training of each module. Currently, multi-target tracking begins to focus on long-term feature extraction of targets, occlusion target processing, association strategy improvement, and end-to-end framework design. Finally, combined with the existing algorithms, this paper summarizes urgent problems to be solved in deep online multi-object tracking and looks forward to possible research directions in the future.

Keywords

• |online multi-object tracking|deep learning|feature extraction|data association