Research progress of detection and multi-object tracking algorithm in intelligent traffic monitoring system

被引：0
作者：
Jin S.-S. [1 ,2 ]
Long W. [1 ]
Hu L.-X. [1 ]
Wang T.-Y. [1 ,2 ]
Pan H. [2 ]
Jiang L.-H. [1 ]
机构：
[1] School of Information Engineering, Huzhou University, Huzhou
[2] Huzhou Institute of Zhejiang University, Huzhou
来源：
Kongzhi yu Juece/Control and Decision | 2023年 / 38卷 / 04期
关键词：
deep learning; intelligent transportation systems; multi-object tracking; object detection; research progress; smart technologies;
D O I：
10.13195/j.kzyjc.2021.1763
中图分类号：
学科分类号：
摘要：
To build the integrated intelligent traffic monitoring system based on the cooperation of human, road, vehicle and cloud, the research of multi-object tracking has wide application potentials. Traditional methods with handcrafted features are hard to fully represent high-level information, making it difficult to track multi-targets in complex scenes. Deep learning with its powerful learning ability, has gradually been used in various industries and fields, setting off a wave of smart technologies. To understand the research progress on the multi-object tracking algorithms based on deep learning, firstly, the pros and cons of three tracking algorithms, namely tracking by detection, joint detection and tracking as well as multi-object tracking with single object tracker, are compared. Then, the applications of multi-object tracking algorithm in intelligent traffic monitoring systems are introduced. Finally, the problems and challenges of multi-object tracking algorithm are concluded, and the growing trend of multi-object algorithms in the intelligent transportation field is discussed and forecasted. © 2023 Northeast University. All rights reserved.
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页码：890 / 901
页数：11
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