Real-time vehicle detection and tracking based on the combination of YOLOv7 and ByteTrack

Yining Zhang

doi:10.54254/2755-2721/4/20230467

Research Article

Open access

Published on 31 May 2023

Download pdf

Zhang,Y. (2023). Real-time vehicle detection and tracking based on the combination of YOLOv7 and ByteTrack. Applied and Computational Engineering,4,267-271.

Export citation

Real-time vehicle detection and tracking based on the combination of YOLOv7 and ByteTrack

Yining Zhang *^,1,

¹ Computer Science and Engineering, University of New South Wales, Sydney, 2052, Australia

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/4/20230467

Abstract

Real-time tracking of vehicles is important for monitoring whether the roads are congested or not. How to achieve and maintain a high frame rate is a key problem to be solved in practical applications. In recent years, SORT, BOT-SORT [1] and other state-of-the-art target tracking algorithms have achieved fruitful results in target tracking tasks. The author combines YOLOv7(You Only Look Once v7) with bytetrack and compares the frame rate with some popular algorithms like Deepsort and bytetrack in real-time tracking to demonstrate that YOLOv7-bytetrack is more suitable for real-time detection, tracking of vehicles.

Keywords

Real-time tracking, high frame rate, YOLOv7, bytetrack.

View pdf

References

[1]. Nir Aharon, Roy Orfaig, Ben-Zion Bobrovsky. BoT-SORT: Robust Associations Multi-Pedestrian Tracking. arXiv:2206.14651. 7. July 2022. 1.

[2]. INRIX. 2017. “TRAFFIC CONGESTION COST UK MOTORISTS over £37.7 BILLION in 2017.” Inrix, inrix.com/press-releases/scorecard-2017-uk/.

[3]. Yu, Steven. 11 Oct. 2019. “Why is target detection plus target tracking necessary in engineering practice?.” Know column, , zhuanlan.zhihu.com/p/70268783.

[4]. Alex Bewley , Zongyuan Ge, Lionel Ott , Fabio Ramos , Ben Upcroft. SIMPLE ONLINE and REALTIME TRACKING. arXiv:1602.00763. 7 July 2017. 1

[5]. Nicolai Wojke, Alex Bewley, Dietrich Paulus. SIMPLE ONLINE and REALTIME TRACKING with a DEEP ASSOCIATION METRIC. arXiv:1703.07402v1. 21 Mar. 2017. 1

[6]. ChuYi, Kaiheng et al. 23 June 2022. “YOLOv6：Fast and accurate target detection framework is open source!.” Tech.meituan.com, tech.meituan.com/2022/06/23/yolov6-a-fast-and-accurate-target-detection-framework-is-opening-source.html.

[7]. Chien-Yao Wang , Alexey Bochkovskiy, and Hong-Yuan Mark Liao. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-The-Art for Real-Time Object Detectors.arXiv:2207.02696v1. 6 July 2022. 1

[8]. Zheng Ge, Songtao Liu, Feng Wang ,Zeming Li, Jian Sun. YOLOX: Exceeding YOLO Series in 2021. arXiv:2107.08430v2. 6 Aug. 2021. 1

[9]. Chien-Yao Wang , Alexey Bochkovskiy , and Hong-Yuan Mark Liao. Scaled-YOLOv4: Scaling Cross Stage Partial Network. In CVPR. 2021. 1

[10]. Jocher, Glenn, et al. “Ultralytics/Yolov5: V6.2 - YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci.ai Integrations.” Zenodo, 17 Aug. 2022, zenodo.org/record/7002879#.YwoEd3bP1D8. Accessed 27 Aug. 2022.

[11]. Yifu Zhang , Peize Sun , Yi Jiang , Dongdong Yu , Fucheng Weng , Zehuan Yuan , Ping Luo , Wenyu Liu , Xinggang Wang . ByteTrack: Multi-Object Tracking by Associating Every Detection Box. arXiv:2110.06864v3. 7 Apr. 2022.

Cite this article

Zhang,Y. (2023). Real-time vehicle detection and tracking based on the combination of YOLOv7 and ByteTrack. Applied and Computational Engineering,4,267-271.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Conference website: http://www.confspml.org

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)

Conference date: 25 February 2023

Editor：Omer Burak Istanbullu

Series: Applied and Computational Engineering

Volume number: Vol.4

ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).