Enhanced Vehicle Tracking Using YOLO 11 and U-Net for Real-Time Segmentation and Identification

DOI

10.2991/978-94-6463-858-5_232How to use a DOI?

Keywords

Vehicle Tracking; YOLO 11; U-Net Segmentation; Deep Learning; Intelligent Transportation

Abstract

Vehicle tracking plays a crucial role in intelligent transportation systems, traffic monitoring, and autonomous driving. Traditional tracking approaches often struggle with occlusions, varying lighting conditions, and com- plex backgrounds. This paper proposes an innovative method that integrates YOLO 11, a state-of-the-art real-time object detection model, with U-Net, a powerful segmentation network, to enhance vehicle tracking accuracy. YOLO 11 enables fast and precise vehicle detection, while U-Net refines segmentation for improved localization and occlusion handling. The proposed approach outperforms conventional tracking methods by leveraging deep learning-based segmentation for fine-grained vehicle recognition. Experimental results demonstrate superior performance in terms of accuracy, robustness, and real-time processing, making it suitable for practical applications in intelligent transportation and smart surveillance systems.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Shanmuga Sundari
AU  - Kbks Durga
AU  - G. Naga Satish
PY  - 2025
DA  - 2025/11/04
TI  - Enhanced Vehicle Tracking Using YOLO 11 and U-Net for Real-Time Segmentation and Identification
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2782
EP  - 2789
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_232
DO  - 10.2991/978-94-6463-858-5_232
ID  - Sundari2025
ER  -