A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving

作者：Shuman Guo, Shichang Wang, Zhenzhong Yang, Lijun Wang, Huawei Zhang, Pengyan Guo, Yuguo Gao, Junkai Guo · 发表于：Applied Sciences · 年份：2022 · DOI：10.3390/app122110741 · 被引用次数：73 · 研究领域：Video Surveillance and Tracking Methods、Impact of Light on Environment and Health、Visual Attention and Saliency Detection

Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms have evolved in recent years as a result of deep learning’s outstanding performance in the field of visual object tracking. There have been a number of evaluations on individual sub-problems, but none that cover the challenges, datasets, and algorithms associated with visual multi-object tracking in autonomous driving scenarios. In this research, we present an exhaustive study of algorithms in the field of visual multi-object tracking over the last ten years, based on a systematic review approach. The algorithm is broken down into three groups based on its structure: methods for tracking by detection (TBD), joint detection and tracking (JDT), and Transformer-based tracking. The research reveals that the TBD algorithm has a straightforward structure, however the correlation between its individual sub-modules is not very strong. To track multiple objects, the JDT technique combines multi-module joint learning with a deep network framework. Transformer-based algorithms have been explored over the past two years, and they have benefits in numerous assessment indicators, as well as tremendous research potential in the area of multi-object tracking. Theoretical support for algorithmic research in adjacent disciplines is provided by this paper. Additionally, the approach we discuss, which uses merely monoc...