Vehicle Detection, Tracking and Counting in Traffic Video Streams Based on the Combination of YOLOv9 and DeepSORT Algorithms
DOI:
https://doi.org/10.62411/faith.3048-3719-115Keywords:
DeepSORT, Multi-Object Tracking, Real-Time Object Detection, Traffic Surveillance, Urban Traffic Monitoring, Vehicle Detection, Vehicle Tracking, YOLOv9Abstract
This paper presents a vehicle detection, tracking, and counting system for urban traffic videos based on integrating YOLOv9s and DeepSORT. The proposed method aims to address challenges such as occlusions, high vehicle density, and identity consistency in surveillance video analysis. A private dataset consisting of annotated traffic videos recorded in Thai Nguyen, Vietnam, was used for evaluation. The YOLOv9s model was selected for its balance between speed and accuracy, while DeepSORT provides robust multi-object tracking using appearance features and Kalman filtering. Experimental results demonstrate that the system achieves a mean Average Precision (mAP at 0.5) of 91.4%, an mAP at 0.5-0.95 of 82.7%, and operates at an average speed of 12.9 frames per second (FPS). Vehicle counting was validated against manually annotated ground truth with an average error rate of less than 4%. These results indicate that the proposed approach is both accurate and efficient for real-time traffic monitoring applications.
Downloads
References
M. D. M. T. Nguyen and M. D. M. T. Nguyen, “Artificial Intelligence for Human Detection, Identification and Tracking: Methods and Applications,” J. Futur. Artif. Intell. Technol., vol. 2, no. 1, pp. 79–94, Apr. 2025, doi: 10.62411/faith.3048-3719-87.
T. C. Vu et al., “Object Detection in Remote Sensing Images Using Deep Learning: From Theory to Applications in Intelligent Transportation Systems,” J. Futur. Artif. Intell. Technol., vol. 2, no. 2, pp. 227–241, Jun. 2025, doi: 10.62411/faith.3048-3719-114.
Y. He, Y. Su, X. Wang, J. Yu, and Y. Luo, “An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy,” Front. Phys., vol. 10, Jan. 2023, doi: 10.3389/fphy.2022.1101923.
I. Ogunrinde and S. Bernadin, “Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network,” Sensors, vol. 24, no. 14, p. 4692, Jul. 2024, doi: 10.3390/s24144692.
J. Zhu, C. Ma, J. Rong, and Y. Cao, “Bird and UAVs Recognition Detection and Tracking Based on Improved YOLOv9-DeepSORT,” IEEE Access, vol. 12, pp. 147942–147957, 2024, doi: 10.1109/ACCESS.2024.3475629.
D. Djarah, A. Benmakhlouf, G. Zidani, and L. Khettache, “Online Multi-object Tracking with YOLOv9 and DeepSORT Optimized by Optical Flow,” Eng. Technol. Appl. Sci. Res., vol. 14, no. 6, pp. 17922–17930, Dec. 2024, doi: 10.48084/etasr.8770.
H. Ghahremannezhad, H. Shi, and C. Liu, “Object Detection in Traffic Videos: A Survey,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 7, pp. 6780–6799, Jul. 2023, doi: 10.1109/TITS.2023.3258683.
M. L. Trinh, D. T. Nguyen, L. Q. Dinh, M. D. Nguyen, D. R. I. M. Setiadi, and M. T. Nguyen, “Unmanned Aerial Vehicles (UAV) Networking Algorithms: Communication, Control, and AI-Based Approaches,” Algorithms, vol. 18, no. 5, p. 244, Apr. 2025, doi: 10.3390/a18050244.
J. Tang, C. Ye, X. Zhou, and L. Xu, “YOLO-Fusion and Internet of Things: Advancing object detection in smart transportation,” Alexandria Eng. J., vol. 107, pp. 1–12, Nov. 2024, doi: 10.1016/j.aej.2024.09.012.
H. T. Do et al., “Energy‐Efficient Unmanned Aerial Vehicle (UAV) Surveillance Utilizing Artificial Intelligence (AI),” Wirel. Commun. Mob. Comput., vol. 2021, no. 1, Jan. 2021, doi: 10.1155/2021/8615367.
L. Qiu, D. Zhang, Y. Tian, and N. Al-Nabhan, “Deep learning-based algorithm for vehicle detection in intelligent transportation systems,” J. Supercomput., vol. 77, no. 10, pp. 11083–11098, Oct. 2021, doi: 10.1007/s11227-021-03712-9.
L. Jiao et al., “A Survey of Deep Learning-Based Object Detection,” IEEE Access, vol. 7, pp. 128837–128868, 2019, doi: 10.1109/ACCESS.2019.2939201.
M. T. Nguyen, L. H. Truong, and T. T. H. Le, “Video Surveillance Processing Algorithms utilizing Artificial Intelligent (AI) for Unmanned Autonomous Vehicles (UAVs),” MethodsX, vol. 8, p. 101472, 2021, doi: 10.1016/j.mex.2021.101472.
A. Kaur, Y. Singh, N. Neeru, L. Kaur, and A. Singh, “A Survey on Deep Learning Approaches to Medical Images and a Systematic Look up into Real-Time Object Detection,” Arch. Comput. Methods Eng., vol. 29, no. 4, pp. 2071–2111, Jun. 2022, doi: 10.1007/s11831-021-09649-9.
M. Saraei, M. Lalinia, and E.-J. Lee, “Deep Learning-Based Medical Object Detection: A Survey,” IEEE Access, vol. 13, pp. 53019–53038, 2025, doi: 10.1109/ACCESS.2025.3553087.
M. T. Nguyen, L. H. Truong, T. T. Tran, and C.-F. Chien, “Artificial intelligence based data processing algorithm for video surveillance to empower industry 3.5,” Comput. Ind. Eng., vol. 148, p. 106671, Oct. 2020, doi: 10.1016/j.cie.2020.106671.
M. Çiftçi, M. U. Türkdamar, and C. Öztürk, “Leveraging YOLO Models for Safety Equipment Detection on Construction Sites,” J. Comput. Theor. Appl., vol. 1, no. 4, pp. 492–506, May 2024, doi: 10.62411/jcta.10453.
V. G. Dhanya et al., “Deep learning based computer vision approaches for smart agricultural applications,” Artif. Intell. Agric., vol. 6, pp. 211–229, 2022, doi: 10.1016/j.aiia.2022.09.007.
Y. Zhang, C. Song, and D. Zhang, “Deep Learning-Based Object Detection Improvement for Tomato Disease,” IEEE Access, vol. 8, pp. 56607–56614, 2020, doi: 10.1109/ACCESS.2020.2982456.
L. Fei and B. Han, “Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review,” Sensors, vol. 23, no. 8, p. 3852, Apr. 2023, doi: 10.3390/s23083852.
S. Y. Alaba and J. E. Ball, “Deep Learning-Based Image 3-D Object Detection for Autonomous Driving: Review,” IEEE Sens. J., vol. 23, no. 4, pp. 3378–3394, Feb. 2023, doi: 10.1109/JSEN.2023.3235830.
B. Mahaur, N. Singh, and K. K. Mishra, “Road object detection: a comparative study of deep learning-based algorithms,” Multimed. Tools Appl., vol. 81, no. 10, pp. 14247–14282, Apr. 2022, doi: 10.1007/s11042-022-12447-5.
I. Martinez-Alpiste, G. Golcarenarenji, Q. Wang, and J. M. Alcaraz-Calero, “Search and rescue operation using UAVs: A case study,” Expert Syst. Appl., vol. 178, p. 114937, Sep. 2021, doi: 10.1016/j.eswa.2021.114937.
A. Nasraoui, T. Selmi, and Z. Hajaiej, “Integration of Deep Learning Object Detection Techniques and Drone Technology for Disaster Monitoring and Detection,” in 2024 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), Apr. 2024, pp. 1–5. doi: 10.1109/IC_ASET61847.2024.10596153.
S. Majchrowska et al., “Deep learning-based waste detection in natural and urban environments,” Waste Manag., vol. 138, pp. 274–284, Feb. 2022, doi: 10.1016/j.wasman.2021.12.001.
M. T. Nguyen, C. V. Nguyen, and H. N. Nguyen, “Visualization-based monitoring in early warning systems with wireless sensor networks,” Indones. J. Electr. Eng. Comput. Sci., vol. 31, no. 1, p. 281, Jul. 2023, doi: 10.11591/ijeecs.v31.i1.pp281-289.
T. Diwan, G. Anirudh, and J. V. Tembhurne, “Object detection using YOLO: challenges, architectural successors, datasets and applications,” Multimed. Tools Appl., vol. 82, no. 6, pp. 9243–9275, Mar. 2023, doi: 10.1007/s11042-022-13644-y.
T. L. Mien, N. D. Tu, and N. Van Lam, “Deploying YOLOv8 for Real-Time Road Crack Detection on Smart Road Length Measurement Devices,” J. Futur. Artif. Intell. Technol., vol. 2, no. 1, pp. 135–144, May 2025, doi: 10.62411/faith.3048-3719-102.
M. Yaseen, “What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector,” ArXiv. Sep. 12, 2024. [Online]. Available: http://arxiv.org/abs/2409.07813
C.-Y. Wang, I.-H. Yeh, and H.-Y. Mark Liao, “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,” in Computer Vision – ECCV 2024, 2025, pp. 1–21. doi: 10.1007/978-3-031-72751-1_1.
Y. Zhao et al., “DETRs Beat YOLOs on Real-time Object Detection,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2024, pp. 16965–16974. doi: 10.1109/CVPR52733.2024.01605.
Y. Chen et al., “YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-Time Object Detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 47, no. 6, pp. 4240–4252, Jun. 2025, doi: 10.1109/TPAMI.2025.3538473.
R. Khanam and M. Hussain, “What is YOLOv5: A deep look into the internal features of the popular object detector,” ArXiv. Jul. 30, 2024. [Online]. Available: http://arxiv.org/abs/2407.20892
M. Bakirci and I. Bayraktar, “YOLOv9-Enabled Vehicle Detection for Urban Security and Forensics Applications,” in 2024 12th International Symposium on Digital Forensics and Security (ISDFS), Apr. 2024, pp. 1–6. doi: 10.1109/ISDFS60797.2024.10527304.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Thang C. Vu, Tung D. Tran, Tao V. Nguyen, Dung T. Nguyen, Long Q. Dinh, Mui D. Nguyen, Hung T. Nguyen, Minh T. Nguyen
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
