Detection and Tracking Different Type of Cars With YOLO model combination and deep sort algorithm based on computer vision of traffic controlling


  • Nisma Novita Hasibuan Universitas Sumatera Utara, Medan, Indonesia
  • Muhammad Zarlis Universitas Sumatera Utara, Indonesia
  • Syahril Efendi Universitas Sumatera Utara, Medan, Indonesia




Traffic surveillance, Urban traffic problems, YOLOv4, Deep Sort algorithm, traffic light controlling


The application of CCTV cameras for traffic surveillance and monitoring is one effective solution to address urban traffic problems, as the number of vehicles that continue to increase rapidly but the area of the road remains the same will cause congestion. However, the problem in traffic surveillance and monitoring is not just focusing on vehicle detection based on category inference on video sequence data sourced from CCTV cameras alone, another important, challenging task is to combine calculations, classification and tracking of different vehicle movements in urban traffic control systems. The study expanded on previous research by breaking down the problem into different sub-tasks using the YOLOv4 approach combined with the Deep Sort algorithm for the detection and tracking of objects directly on CCTV footage of vehicle activity on the city's three-stop highway.   Based on the results of YOLOv4 testing resulted in a detection accuracy rate with mAP of 87.98% where the combination of YOLOv4 with the Deep Sort algorithm can detect, track and calculate 13 types of vehicles.

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Arcos-García, Álvaro, Juan A. Álvarez-García, and Luis M. Soria-Morillo. 2018. “Evaluation of Deep Neural Networks for Traffic Sign Detection Systems.” Neurocomputing 316:332–44. doi: 10.1016/j.neucom.2018.08.009.

Asha, C. S., and A. V Narasimhadhan. 2018. “Vehicle Counting for Traffic Management System Using YOLO and Correlation Filter.” Pp. 1–6 in 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE.

Bewley, Alex, Zongyuan Ge, Lionel Ott, Fabio Ramos, and Ben Upcroft. 2016. “Simple Online and Realtime Tracking.” Pp. 3464–68 in Proceedings - International Conference on Image Processing, ICIP. Vols. 2016-Augus. IEEE Computer Society.

Biswas, Debojit, Hongbo Su, Chengyi Wang, Aleksandar Stevanovic, and Weimin Wang. 2019. “An Automatic Traffic Density Estimation Using Single Shot Detection (SSD)and MobileNet-SSD.” Physics and Chemistry of the Earth 110(January 2019):176–84. doi: 10.1016/j.pce.2018.12.001.

Bochinski, Erik, Volker Eiselein, and Thomas Sikora. 2017. “High-Speed Tracking-by-Detection without Using Image Information.” 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 (August). doi: 10.1109/AVSS.2017.8078516.

Bochkovskiy, Alexey, Chien Yao Wang, and Hong Yuan Mark Liao. 2020. “YOLOv4: Optimal Speed and Accuracy of Object Detection.” ArXiv.

Choi, Jiwoong, Dayoung Chun, Hyun Kim, and Hyuk Jae Lee. 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:502–11. doi: 10.1109/ICCV.2019.00059.

Dai, Zhe, Huansheng Song, Xuan Wang, Yong Fang, Xu Yun, Zhaoyang Zhang, and Huaiyu Li. 2019. “Video-Based Vehicle Counting Framework.” IEEE Access 7:64460–70. doi: 10.1109/ACCESS.2019.2914254.

Duan, Kaiwen, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, and Qi Tian. 2019. “CenterNet: Keypoint Triplets for Object Detection.” Proceedings of the IEEE International Conference on Computer Vision 2019-Octob:6568–77. doi: 10.1109/ICCV.2019.00667.

Gu, Xiao Feng, Zi Wei Chen, Ting Song Ma, Fan Li, and Long Yan. 2017. “Real-Time Vehicle Detection and Tracking Using Deep Neural Networks.” 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017 167–70. doi: 10.1109/ICCWAMTIP.2016.8079830.

Han, Byung Gil, Joon Goo Lee, Kil Taek Lim, and Doo Hyun Choi. 2020. “Design of a Scalable and Fast Yolo for Edge-Computing Devices.” Sensors (Switzerland) 20(23):1–15. doi: 10.3390/s20236779.

He, Zhiqun, Yu Lei, Shuai Bai, and Wei Wu. 2019. “Multi-Camera Vehicle Tracking with Powerful Visual Features and Spatial-Temporal Cue.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 10.

Hsu, Shih Chung, Chung Lin Huang, and Cheng Hung Chuang. 2018. “Vehicle Detection Using Simplified Fast R-CNN.” 2018 International Workshop on Advanced Image Technology, IWAIT 2018 1–3. doi: 10.1109/IWAIT.2018.8369767.

Huang, Yi Qi, Jia Chun Zheng, Shi Dan Sun, Cheng Fu Yang, and Jing Liu. 2020. “Optimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections.” Applied Sciences (Switzerland) 10(9):3079. doi: 10.3390/app10093079.

Husein, A. M., Christopher Christopher, Andy Gracia, Rio Brandlee, and Muhammad Haris Hasibuan. 2020. “Deep Neural Networks Approach for Monitoring Vehicles on the Highway.” SinkrOn 4(2):163. doi: 10.33395/sinkron.v4i2.10553.

Kim, Jong Bae. 2019. “Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera.” Symmetry 11(10):8–10. doi: 10.3390/sym11101205.

Lai, Yanyu, Fuchun Sun, and Huaping Liu. 2020. “Small Object Detection Base on YOLOv3 for Pedestrian Recognition.” Pp. 235–41 in 2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020.

Laroca, Rayson, Luiz A. Zanlorensi, Gabriel R. Gonçalves, Eduardo Todt, William Robson Schwartz, and David Menotti. 2019. “An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO Detector.” ArXiv.

Law, Hei, and Jia Deng. 2020. “CornerNet: Detecting Objects as Paired Keypoints.” International Journal of Computer Vision 128(3):642–56. doi: 10.1007/s11263-019-01204-1.

Li, Xun, Yao Liu, Zhengfan Zhao, Yue Zhang, and Li He. 2018. “A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video.” Journal of Advanced Transportation 2018. doi: 10.1155/2018/7075814.

Lin, Tsung Yi, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. 2020. “Focal Loss for Dense Object Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42(2):318–27. doi: 10.1109/TPAMI.2018.2858826.

Liu, Fei, Zhiyuan Zeng, and Rong Jiang. 2017. “A Video-Based Real-Time Adaptive Vehicle-Counting System for Urban Roads” edited by X. Hu. PLoS ONE 12(11):1–16. doi: 10.1371/journal.pone.0186098.

Mahto, Pooja, Priyamm Garg, Pranav Seth, and J. Panda. 2020. “Refining Yolov4 for Vehicle Detection.” International Journal of Advanced Research in Engineering and Technology 11(5):409–19. doi: 10.34218/IJARET.11.5.2020.043.

Meng, Qiao, Huansheng Song, Yu’An Zhang, Xiangqing Zhang, Gang Li, and Yanni Yang. 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. doi: 10.1155/2020/1969408.

Mu, Kenan, Fei Hui, and Xiangmo Zhao. 2016. “Multiple Vehicle Detection and Tracking in Highway Traffic Surveillance Video Based on Sift Feature Matching.” Journal of Information Processing Systems 12(2):183–95.

Nam Bui, Khac Hoai, Hongsuk Yi, and Jiho Cho. 2020. “A Multi-Class Multi-Movement Vehicle Counting Framework for Traffic Analysis in Complex Areas Using CCTV Systems.” Energies 13(8). doi: 10.3390/en13082036.

Nguyen, Hoanh. 2019. “Improving Faster R-CNN Framework for Fast Vehicle Detection.” Mathematical Problems in Engineering 2019. doi: 10.1155/2019/3808064.

Oltean, Gabriel, Camelia Florea, Radu Orghidan, and Victor Oltean. 2019. “Towards Real Time Vehicle Counting Using YOLO-Tiny and Fast Motion Estimation.” SIITME 2019 - 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging, Proceedings 240–43. doi: 10.1109/SIITME47687.2019.8990708.

Ouyang, Lecheng, and Huali Wang. 2019. “Vehicle Target Detection in Complex Scenes Based on YOLOv3 Algorithm.” IOP Conference Series: Materials Science and Engineering 569(5). doi: 10.1088/1757-899X/569/5/052018.

Rublee, Ethan, Vincent Rabaud, Kurt Konolige, and Gary Bradski. 2011. “ORB: An Efficient Alternative to SIFT or SURF.” Pp. 2564–71 in 2011 International Conference on Computer Vision. Vol. 2008. IEEE.

Sang, Jun, Zhongyuan Wu, Pei Guo, Haibo Hu, Hong Xiang, Qian Zhang, and Bin Cai. 2018. “An Improved YOLOv2 for Vehicle Detection.” Sensors (Switzerland) 18(12). doi: 10.3390/s18124272.

Schumann, Arne, Lars Sommer, Krassimir Valev, and Jurgen Beyerer. 2018. “A Systematic Evaluation of Recent Deep Learning Architectures for Fine-Grained Vehicle Classification.” P. 1 in Pattern Recognition and Tracking XXIX, edited by M. S. Alam. SPIE.

Song, Huansheng, Haoxiang Liang, Huaiyu Li, Zhe Dai, and Xu Yun. 2019. “Vision-Based Vehicle Detection and Counting System Using Deep Learning in Highway Scenes.” European Transport Research Review 11(1). doi: 10.1186/s12544-019-0390-4.

Tan, Mingxing, and Quoc V. Le. 2019. “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.” 36th International Conference on Machine Learning, ICML 2019 2019-June:10691–700.

Tan, Mingxing, Ruoming Pang, and Quoc V. Le. 2020. “EfficientDet: Scalable and Efficient Object Detection.” Pp. 10778–87 in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society.

Wang, Xiaolan, Shuo Wang, Jiaqi Cao, and Yansong Wang. 2020. “Data-Driven Based Tiny-YOLOv3 Method for Front Vehicle Detection Inducing SPP-Net.” IEEE Access 8:110227–36. doi: 10.1109/ACCESS.2020.3001279.

Wang, Xinqing, Xia Hua, Feng Xiao, Yuyang Li, Xiaodong Hu, and Pengyu Sun. 2018. “Multi-Object Detection in Traffic Scenes Based on Improved SSD.” Electronics (Switzerland) 7(11). doi: 10.3390/electronics7110302.

Watkins, Rohan, Nick Pears, and Suresh Manandhar. 2018. “Vehicle Classification Using ResNets, Localisation and Spatially-Weighted Pooling.” ArXiv.

Wei, Runchen, Ning He, and Ke Lu. 2020. “YOLO-Mini-Tiger: Amur Tiger Detection.” Pp. 517–24 in ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval.

Wojke, Nicolai, Alex Bewley, and Dietrich Paulus. 2018. “Simple Online and Realtime Tracking with a Deep Association Metric.” Pp. 3645–49 in Proceedings - International Conference on Image Processing, ICIP. Vols. 2017-Septe.

Wu, Fan, Guoqing Jin, Mingyu Gao, Zhiwei He, and Yuxiang Yang. 2019. “Helmet Detection Based on Improved YOLO V3 Deep Model.” Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019 363–68. doi: 10.1109/ICNSC.2019.8743246.

Xiang, Xuezhi, Ning Lv, Xinli Guo, Shuai Wang, and Abdulmotaleb El Saddik. 2018. “Engineering Vehicles Detection Based on Modified Faster R-CNN for Power Grid Surveillance.” Sensors (Switzerland) 18(7). doi: 10.3390/s18072258.

Xiang, Xuezhi, Mingliang Zhai, Ning Lv, and Abdulmotaleb El Saddik. 2018. “Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos.” Sensors (Switzerland) 18(8):1–17. doi: 10.3390/s18082560.

Zhang, Fukai, Ce Li, and Feng Yang. 2019. “Vehicle Detection in Urban Traffic Surveillance Images Based on Convolutional Neural Networks with Feature Concatenation.” Sensors (Switzerland) 19(3). doi: 10.3390/s19030594.

Zhang, Shifeng, Longyin Wen, Xiao Bian, Zhen Lei, and Stan Z. Li. 2018. “Single-Shot Refinement Neural Network for Object Detection.” Pp. 4203–12 in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Zhang, Xiaosong, Fang Wan, Chang Liu, Xiangyang Ji, and Qixiang Ye. 2021. “Learning to Match Anchors for Visual Object Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: 10.1109/TPAMI.2021.3050494.

Zhao, Xia, Yingting Ni, and Haihang Jia. 2017. “Modified Object Detection Method Based on YOLO.” Pp. 233–44 in.

Zhou, Jun, Yichen Tian, Chao Yuan, Kai Yin, Guang Yang, and Meiping Wen. 2019. “Improved UAV Opium Poppy Detection Using an Updated YOLOV3 Model.” Sensors (Switzerland) 19(22):1–23. doi: 10.3390/s19224851.

Zhu, Jianxiao, Xu Li, Peng Jin, Qimin Xu, Zhengliang Sun, and Xiang Song. 2021. “MME-YOLO: Multi-Sensor Multi-Level Enhanced Yolo for Robust Vehicle Detection in Traffic Surveillance.” Sensors (Switzerland) 21(1):1–17. doi: 10.3390/s21010027.


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How to Cite

Hasibuan, N. N. ., Zarlis, M. ., & Efendi, S. . (2021). Detection and Tracking Different Type of Cars With YOLO model combination and deep sort algorithm based on computer vision of traffic controlling. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(1), 210-221.

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