Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
609-615,622

Detection of traffic lights for off-line videos based on network flow tracking

Wu Yue1
Chen Haihua1,2
Yu Qiaofeng3
1. College of Electronic Information & Optical Engineering, Nankai University, Tianjin 300350, China
2. Tianjin Key Laboratory of Optoelectronic Sensor & Sensing Network Technology, Tianjin 300350, China
3. Beijing Branch of China Telecom Cybersecurity Tech Co. , Ltd. , Beijing 100000, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0301

Abstract

Combining the traffic light information to guide the speed of vehicles and reducing the number of starts and stops of vehicles can effectively reduce exhaust emissions and alleviate pollution problems caused by them. Aiming at the acquisition of traffic light transition time, this paper proposed a traffic light detection method based on network flow tracking. It introduced auxiliary traffic light class in the dataset for training, and correlated the detection results of this class in the video sequence as a tracklet, then modeled the multi-object tracking task by the tracklet. Specifically, this method converted the multi-target tracking task into the minimum cost flow optimization task. This method established the minimum cost flow network with the tracklet as the node, and proposed a cost construction method suitable for traffic lights, then obtained multiple traces of the auxiliary traffic light in the video sequence by solving the shortest path algorithm. Based on the solved trajectory results and image classification technology, it finally improved the performance of traffic light detection. Compared with the comparison algorithms, the proposed method greatly improves the tracking performance, and increases the mAP of the small target traffic light detection response to 94.35%. Experimental results show that the network flow modeling method can greatly improve the tracking effect of the auxiliary traffic light. Combined with the tracking results, it greatly improves the detection accuracy of the small target traffic light in the video sequence and effectively determines the transition time of traffic light status.

Foundation Support

国家自然科学基金资助项目(61973173)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0301
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Technology of Graphic & Image
Pages: 609-615,622
Serial Number: 1001-3695(2024)02-044-0609-07

Publish History

[2023-09-06] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

武悦, 陈海华, 于乔烽. 基于网络流跟踪的信号灯检测方法 [J]. 计算机应用研究, 2024, 41 (2): 609-615,622. (Wu Yue, Chen Haihua, Yu Qiaofeng. Detection of traffic lights for off-line videos based on network flow tracking [J]. Application Research of Computers, 2024, 41 (2): 609-615,622. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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