Integrated Approach for Vehicle Detection, Tracking, and Counting in Urban Traffic Videos Utilizing YOLOv11 and DeepSORT Algorithms
Tác giả
Tài liệu tham khảo
[1] Tang, L., Yun, L., Chen, Z., Cheng, F.: HRYNet: a highly robust YOLO network for complex road traffic object detection. Sensors 24(2), 642 (2024)
[2] Wang, C., Zheng, B., Li, C.: Efficient traffic sign recognition using YOLO for intelligent transport systems. Sci. Rep. 15(1), 13657 (2025)
[3] Vu, T.C., et al.: Object detection in remote sensing images using deep learning: from theory to applications in intelligent transportation systems. J. Future Artif. Intell. Technol. 2(2), 227–241 (2025)
[4] Hosain, Md.T., Zaman, A., Abir, M.R., Akter, S., Mursalin, S., Khan, S.S.: Synchronizing object detection: applications, advancements and existing challenges. IEEE Access 12, 54129–54167 (2024)
[5] Tennekoon, S., Wedasingha, N., Welhenge, A., Abhayasinghe, N., Murray, I.: Advancing object detection: a narrative review of evolving techniques and their navigation applications. IEEE Access (2025)
[6] Ghahremannezhad, H., Shi, H.: Object detection in traffic videos: a survey. IEEE Trans. Intell. Transp. Syst. 24(7), 6780–6799 (2023)
[7] Trinh, M.L., Nguyen, D.T., Dinh, L.Q., Nguyen, M.D., Setiadi, D.R.I.M., Nguyen, M.T.: Unmanned aerial vehicles (UAV) networking algorithms: communication, control, and AI-based approaches. Algorithms 18(5), 244 (2025)
[8] Nguyen, M.D., Nguyen, M.T.: Artificial intelligence for human detection, identification and tracking: methods and applications. J. Future Artif. Intell. Technol. 2(1), 79–94 (2025)
[9] Wang, Y., Pan, L., Shu, X.: YOLO-FMS: a lightweight and efficient model for medical microscopic smear detection. IEEE Access (2024)
[10] Do, H.T., et al.: Energy-efficient unmanned aerial vehicle (UAV) surveillance utilizing artificial intelligence (AI). Wirel. Commun. Mob. Comput. 2021(1), 8615367 (2021)
[11] Kang, S., Hu, Z., Liu, L., Zhang, K., Cao, Z.: Object detection YOLO algorithms and their industrial applications: overview and comparative analysis. Electronics 14(6), 1104 (2025)
[12] Al Rabbani Alif, M., Hussain, M.: YOLOv1 to YOLOv10: a comprehensive review of YOLO variants and their application in the agricultural domain. arXiv preprint arXiv:2406.10139 (2024)
[13] Nguyen, M.T., Truong, L.H., Le, T.T.H.: Video surveillance processing algorithms utilizing artificial intelligent (AI) for unmanned autonomous vehicles (UAVs). MethodsX 8, 101472 (2021)
[14] Tang, J., Ye, C., Xu, L.: YOLO-fusion and Internet of Things: advancing object detection in smart transportation. Alex. Eng. J. 107, 1–12 (2024)
[15] Saraei, M., Lalinia, M., Lee, E.-J.: Deep learning-based medical object detection: a survey. IEEE Access (2025)
[16] Nguyen, M.T., Truong, L.H., Tran, T.T., Chien, C.-F.: Artificial intelligence based data processing algorithm for video surveillance to empower Industry 3.5. Comput. Ind. Eng. 148, 106671 (2020)
[17] Abdelnabi, A.A.B.: Human detection from unmanned aerial vehicles’ images for search and rescue missions: a state-of-the-art review. IEEE Access (2024)
[18] Shoaib, M., et al.: Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease. Front. Plant Sci. 13, 1031748 (2022)
[19] Diwan, T., Anirudh, G., Tembhurne, J.V.: Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimed. Tools Appl. 82(6), 9243–9275 (2023)
[20] Alaba, S.Y., Ball, J.E.: Deep learning-based image 3-D object detection for autonomous driving. IEEE Sens. J. 23(4), 3378–3394 (2023)
[21] Chandana, R.K., Ramachandra, A.C.: Real time object detection system with YOLO and CNN models: a review. arXiv Prepr. arXiv2208 773 (2022)
[22] Shafiee, M.J., Chywl, B., Li, F., Wong, A.: Fast YOLO: a fast you only look once system for real-time embedded object detection in video. arXiv preprint arXiv:1709.05943 (2017)
[23] Jacutprakart, J.: Advanced deep learning towards improving prediction outcomes on medical imaging data and radiology reports. Ph.D. diss., University of Essex (2025)
[24] Dhanya, V.G., et al.: Deep learning based computer vision approaches for smart agricultural applications. Artif. Intell. Agric. 6, 211–229 (2022)
[25] Sorenson, H.W.: Least-squares estimation: from Gauss to Kalman. IEEE Spectr. 7(7), 63–68 (2009)