Traffic Impact Assessment System using Yolov5 and ByteTrack
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Abstract
Monitoring software for traffic is not too much in this era of digital. Even cheaper is decent traffic monitoring software. You can gauge the quality of the software. It should be possible to assess the code's performance outside of a test environment. The most useful metrics are frequently those that support the program's ability to fulfil business requirements. Therefore, this project is planning to develop a traffic assessment system. The main purpose of development is to improve heavy traffic in this country – Malaysia. This system includes function vehicle detection using YOLOv5, vehicle counting with a different type (such as bus, car, truck), vehicle classification, vehicle idling time by each region, and vehicle counting for each junction. Users can draw regions and lines for each camera/video to count and record vehicles. After the traffic analysis, intelligent signal light systems that respond to loads and timing can be helpful in easing traffic congestion. Smart traffic lights may adapt to the patterns of bustle at junctions and other important road traffic places based on the number of cars, data from queue detectors, and images from cameras. Also, this report includes comparisons with StrongSORT, OC-SORT and ByteTrack and accuracy test for vehicle counting.
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References
E. Teh, “Traffic Congestions in Malaysia and the Lessons We Must Learn | Heinrich Böll Foundation | Southeast Asia Regional Office,” Heinrich-Böll-Stiftung, Jul. 22, 2022. https://th.boell.org/en/2022/07/22/traffic-malaysia
M. Armit, “Traffic Impact Assessment Report: All you Need to Know,” Medium, Oct. 14, 2016. https://medium.com/@michaelarmit3/traffic-impact-assessment-report-all-you-need-to-know-7613c81b8587
G. Wieczorek, S. B. ud din Tahir, I. Akhter, and J. Kurek, “Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization,” Sensors, vol. 23, no. 3, p. 1731, Feb. 2023, doi: https://doi.org/10.3390/s23031731.
H. Song, H. Liang, H. Li, Z. Dai, and X. Yun, “Vision-based vehicle detection and counting system using deep learning in highway scenes,” European Transport Research Review, vol. 11, no. 1, Dec. 2019, doi: https://doi.org/10.1186/s12544-019-0390-4.
Saran K B and Sreelekha G, "Traffic video surveillance: Vehicle detection and classification," 2015 International Conference on Control Communication & Computing India (ICCC), Trivandrum, India, 2015, pp. 516-521, doi: 10.1109/ICCC.2015.7432948.
M. Fachrie, “A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model”, J. RESTI (Rekayasa Sist. Teknol. Inf.) , vol. 4, no. 3, pp. 462 - 468, Jun. 2020.
N. Wojke, A. Bewley, and D. Paulus, “Simple Online and Realtime Tracking with a Deep Association Metric,” arXiv (Cornell University), Mar. 2017, doi: 10.48550/arxiv.1703.07402.
“Understanding Multiple Object Tracking using DeepSORT,” Jun. 21, 2022. https://learnopencv.com/understanding-multiple-object-tracking-using-deepsort/
M. Gaikwad, “Deep Sort: Simple Online and Real-time Tracking with Deep Associative Metric,” Medium, Apr. 21, 2023. https://medium.com/@mohit_gaikwad/deep-sort-simple-online-and-real-time-tracking-with-deep-associative-metric-94138d528ff1 (accessed Aug. 18, 2023).
Y. Du et al., “StrongSORT: Make DeepSORT Great Again,” IEEE Transactions on Multimedia, pp. 1–14, 2023, doi: https://doi.org/10.1109/tmm.2023.3240881.
S. Sah, “Real time Object tracking and Segmentation using YoloV8 with Strongsort, Ocsort and Bytetrack,” Medium, Jun. 08, 2023. https://siddharthksah.medium.com/real-time-object-tracking-and-segmentation-using-yolov8-with-strongsort-ocsort-and-bytetrack-180eef43354a (accessed Aug. 18, 2023).
Y. Zhang et al., “ByteTrack: Multi-Object Tracking by Associating Every Detection Box,” arXiv (Cornell University), Oct. 2021, doi: https://doi.org/10.48550/arxiv.2110.06864.
B. Le, “An Introduction to BYTETrack: Multi-Object Tracking by Associating Every Detection Box,” www.datature.io. https://www.datature.io/blog/introduction-to-bytetrack-multi-object-tracking-by-associating-every-detection-box (accessed Aug. 18, 2023).
J. Cao, X. Weng, Rawal Khirodkar, J. Pang, and K. M. Kitani, “Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking,” Mar. 2022, doi: https://doi.org/10.48550/arxiv.2203.14360.
I. Berrios, “Introduction to OC-SORT,” Medium, Apr. 17, 2023. https://medium.com/@itberrios6/introduction-to-ocsort-c1ea1c6adfa2#25ad (accessed Aug. 18, 2023).
G. Jocher et al., “ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations,” Semantic Scholar, 2021. https://www.semanticscholar.org/paper/ultralytics%2Fyolov5%3A-v5.0-YOLOv5-P6-1280-models%2C-and-Jocher-Stoken/fd550b29c0efee17be5eb1447fddc3c8ce66e838
M. Broström, “Real-time multi-object, segmentation and pose tracking using Yolov8 with DeepOCSORT and LightMBN,” GitHub, Jun. 11, 2023. https://github.com/mikel-brostrom/yolo_tracking
G. Jocher, “ultralytics/yolov5,” GitHub, Aug. 21, 2020. https://github.com/ultralytics/yolov5
F.F. Chua, T.Y. Lim, B. Tajuddin and A.P. Yanuarifiani, “Incorporating Semi-Automated Approach for Effective Software Requirements Prioritization: A Framework Design”, Journal of Informatics and Web Engineering (JIWE), Vol.1 (1), pp. 1-15, 2022.
M.K. Steven Loh and C. E. Zarina, “A Systematic Review on Non-Functional Requirements Documentation in Agile”, Journal of Informatics and Web Engineering (JIWE), Vol.1(2), pp. 19-29, 2022.