Computer Vision-Based Intelligent Traffic Surveillance: Multi-Vehicle Tracking and Detection
DOI:
10.33395/sinkron.v9i1.13204Keywords:
Computer Vision; Deep Learning; Traffic Surveillance; Vehicle Detection; YOLOv4Abstract
The application of vehicle detection in real-time traffic surveillance systems is one of the challenging research fields with different objectives. One of the problems is the detection of many vehicles simultaneously in a video sequence sourced from CCTV cameras. In many works, the focus is only on detecting vehicle classes such as motorcycles, buses, trucks, and cars or special vehicles such as ambulances and others. In this research, we propose to apply 13 classes of vehicle types and implement YOLOv4 in the traffic surveillance task. More specifically, all classes are labeled, and then the YOLOv4 model is trained on 800 images and tested on 23 videos from three intersections in Medan City, namely Juanda Katamso Intersection, Gatot Subroto Intersection, and Uniland Intersection. Based on the test results, YOLOv4 proves successful in detecting many vehicles in frame-by-frame sequence with various types of vehicles. All vehicle detection data will be stored in the file.
Downloads
References
Ammar, A., Koubaa, A., Ahmed, M., Saad, A., & Benjdira, B. (2021). Vehicle detection from aerial images using deep learning: A comparative study. Electronics (Switzerland), 10(7), 1–31. https://doi.org/10.3390/electronics10070820
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. ArXiv. Retrieved from http://arxiv.org/abs/2004.10934
Butt, M. A., Khattak, A. M., Shafique, S., Hayat, B., Abid, S., Kim, K. Il, … Adnan, A. (2021). Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems. Complexity, 2021. https://doi.org/10.1155/2021/6644861
Choi, J., Chun, D., Kim, H., & Lee, H. J. (2019). Gaussian YOLOv3: An accurate and fast object detector using localization uncertainty for autonomous driving. Proceedings of the IEEE International Conference on Computer Vision, 2019-Octob, 502–511. https://doi.org/10.1109/ICCV.2019.00059
Du, S., Zhang, P., Zhang, B., & Xu, H. (2021). Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4. IEEE Access, 9, 25671–25680. https://doi.org/10.1109/ACCESS.2021.3057723
Han, B. G., Lee, J. G., Lim, K. T., & Choi, D. H. (2020). Design of a scalable and fast yolo for edge-computing devices. Sensors (Switzerland), 20(23), 1–15. https://doi.org/10.3390/s20236779
Huang, Y. Q., Zheng, J. C., Sun, S. D., Yang, C. F., & Liu, J. (2020). Optimized YOLOv3 algorithm and its application in traffic flow detections. Applied Sciences (Switzerland), 10(9), 3079. https://doi.org/10.3390/app10093079
Husein, A. M., Christopher, C., Gracia, A., Brandlee, R., & Hasibuan, M. H. (2020). Deep Neural Networks Approach for Monitoring Vehicles on the Highway. SinkrOn, 4(2), 163. https://doi.org/10.33395/sinkron.v4i2.10553
Kasper-Eulaers, M., Hahn, N., Kummervold, P. E., Berger, S., Sebulonsen, T., & Myrland, Ø. (2021). Short communication: Detecting heavy goods vehicles in rest areas in winter conditions using YOLOv5. Algorithms, 14(4). https://doi.org/10.3390/a14040114
Li, X., Liu, Y., Zhao, Z., Zhang, Y., & He, L. (2018). A deep learning approach of vehicle multitarget detection from traffic video. Journal of Advanced Transportation, 2018. https://doi.org/10.1155/2018/7075814
Liang, R., & Ji, G. (2022). Vehicle Detection Algorithm Based on Embedded Video Image Processing in the Background of Information Technology. Journal of Electrical and Computer Engineering, 2022. https://doi.org/10.1155/2022/6917421
Liu, C., Zhang, Y., Luo, H., Tang, J., Chen, W., Xu, X., … Shen, Y. D. (2021). City-scale multi-camera vehicle tracking guided by crossroad zones. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (1), 4124–4132. https://doi.org/10.1109/CVPRW53098.2021.00466
Liu, Y., & Zhang, G. (2021). Vehicle detection algorithm based on LW-SSD. Journal of Physics: Conference Series, 1748(3). https://doi.org/10.1088/1742-6596/1748/3/032042
Lu, J., Xia, M., Gao, X., Yang, X., Tao, T., Meng, H., … DIng, E. (2021). Robust and online vehicle counting at crowded intersections. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 3997–4003. https://doi.org/10.1109/CVPRW53098.2021.00451
Luo, J., Fang, H., Shao, F., Zhong, Y., & Hua, X. (2021). Multi-scale traffic vehicle detection based on faster R–CNN with NAS optimization and feature enrichment. Defence Technology, 17(4), 1542–1554. https://doi.org/10.1016/j.dt.2020.10.006
Mahto, P., Garg, P., Seth, P., & Panda, J. (2020). Refining Yolov4 for vehicle detection. International Journal of Advanced Research in Engineering and Technology, 11(5), 409–419. https://doi.org/10.34218/IJARET.11.5.2020.043
Meng, Q., Song, H., Zhang, Y., Zhang, X., Li, G., & Yang, Y. (2020). Video-Based Vehicle Counting for Expressway: A Novel Approach Based on Vehicle Detection and Correlation-Matched Tracking Using Image Data from PTZ Cameras. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/1969408
Nam Bui, K. H., Yi, H., & Cho, J. (2020). A multi-class multi-movement vehicle counting framework for traffic analysis in complex areas using CCTV systems. Energies, 13(8). https://doi.org/10.3390/en13082036
Neupane, B., Horanont, T., & Aryal, J. (2022). Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network. Sensors, 22(10), 1–21. https://doi.org/10.3390/s22103813
Sun, L., Zhang, J., Yang, Z., & Fan, B. (2023). A Motion-Aware Siamese Framework for Unmanned Aerial Vehicle Tracking. Drones, 7(3), 1–24. https://doi.org/10.3390/drones7030153
Tian, E., & Kim, J. (2023). Improved Vehicle Detection Using Weather Classification and Faster R-CNN with Dark Channel Prior. Electronics (Switzerland), 12(14). https://doi.org/10.3390/electronics12143022
Wang, X., Wang, S., Cao, J., & Wang, Y. (2020). Data-Driven Based Tiny-YOLOv3 Method for Front Vehicle Detection Inducing SPP-Net. IEEE Access, 8, 110227–110236. https://doi.org/10.1109/ACCESS.2020.3001279
Yang, H., Zhang, Y., Zhang, Y., Meng, H., Li, S., & Dai, X. (2021). A Fast Vehicle Counting and Traffic Volume Estimation Method Based on Convolutional Neural Network. IEEE Access, 9, 150522–150531. https://doi.org/10.1109/ACCESS.2021.3124675
Yu, B., Shin, J., Kim, G., Roh, S., & Sohn, K. (2021). Non-Anchor-Based Vehicle Detection for Traffic Surveillance Using Bounding Ellipses. IEEE Access, 9, 123061–123074. https://doi.org/10.1109/ACCESS.2021.3109258
Zhao, J., Hao, S., Dai, C., Zhang, H., Zhao, L., Ji, Z., & Ganchev, I. (2022). Improved Vision-Based Vehicle Detection and Classification by Optimized YOLOv4. IEEE Access, 10(i), 8590–8603. https://doi.org/10.1109/ACCESS.2022.3143365
Zhao, M., Zhong, Y., Sun, D., & Chen, Y. (2021). Accurate and efficient vehicle detection framework based on SSD algorithm. IET Image Processing, 15(13), 3094–3104. https://doi.org/10.1049/ipr2.12297
Zhu, J., Li, X., Jin, P., Xu, Q., Sun, Z., & Song, X. (2021). MME-YOLO: Multi-sensor multi-level enhanced yolo for robust vehicle detection in traffic surveillance. Sensors (Switzerland), 21(1), 1–17. https://doi.org/10.3390/s21010027
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2023 Amir Mahmud Husein, Kevi Lubis, Daniel Sidabutar, Yansan Yuanda, Kevry
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.