Automatic Vehicle Detection and Tracking Strategy Using Deep Learning Model (YOLO v2 & R-CNN)

Authors

  • Priyanka Ankireddy Department of Information Technology, Hindustan Institute of Technology and Science, Chennai, Tamilnadu-603103, India.
  • S. Gopalakrishnan Department of Information Technology, Hindustan Institute of Technology and Science, Chennai, Tamilnadu-603103, India.
  • V. Lokeswara Reddy Department of Computer science and Engineering, KSRM College of Engineering, Kadapa, Andhra Pradesh-516003, India.

Keywords:

Vehicle detection, Deep learning, Quicker R-CNN, YOLOv2

Abstract

Vehicle detection and tracking plays a crucial role in various smart mobility networkss, including traffic monitoring, security surveillance, and autonomous vehicles. Image processing techniques offer a powerful tool for achieving these tasks by analyzing visual information captured from cameras. Since Deep Learning (DL) is developing so quickly, the computer vision community has demanded that excellent, reliable, and efficient services be developed across a range of domains. The objective of vehicle detection and tracking is to automatically detect and track the movement of vehicles in real-time video sequences. The paper presents novel vehicle detection and tracking system using image processing techniques. The approach consists of the key stages: An assortment of image processing techniques was employed in the production of this data. Utilizing the most recent iteration of the YOLO model, YOLO v2, as well as the R-CNN model and Fast-RCN, have all been employed for detection purposes. Following the identification and tracking processes, the number of vehicles and their estimated speeds are calculated. Moreover, the proposed approach using Fast-RCNN has the best performance with precision of 98.94% and recall of 99.12% for the CDnet 2014 dataset respectively..

Downloads

Download data is not yet available.

References

Al-Smadi, M., Abdulrahim, K., Salam, R.A. (2016). Traffic surveillance: A review of vision based vehicle detection, recognition and tracking. International Journal of Applied Engineering Research, 11(1), 713–726.

Radhakrishnan, M. (2013). Video object extraction by using background subtraction techniques for sports applications. Digital Image Processing, 5(9), 91–97.

Qiu-Lin, L.I., & Jia-Feng, H.E. (2011). Vehicles detection based on three-frame-difference method and cross-entropy threshold method. Computer Engineering, 37(4), 172–174.

Liu, Y., Yao, L., Shi, Q., Ding, J. (2014). Optical flow based urban road vehicle tracking, In 2013 Ninth International Conference on Computational Intelligence and Security. https://doi.org/10.1109/ cis.2013.89: IEEE

Dalal, N.; Triggs, B. Histograms of oriented gradients for human detection. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, 20– 25 June 2005; Volume 1, pp. 886–893.

Mita, T.; Kaneko, T.; Hori, O. Joint haar-like features for face detection. In Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV’05), Beijing, China, 17–21 October 2005; Volume 2, pp. 1619–1626.

Zhang, G.; Huang, X.; Li, S.Z.; Wang, Y.; Wu, X. Boosting local binary pattern (LBP)-based face recognition. In Proceedings of the Chinese Conference on Biometric Recognition, Guangzhou, China, 13–14 December 2004; Springer: Berlin/Heidelberg, Germany, 2004; pp. 179–186.

Zhang, Yaoming & Song, Xiaoli & Wang, Mengen & Guan, Tian & Liu, Jiawei & Wang, Zhaojian & Zhen, Yajing & Zhang, Dongsheng & Gu, Xiaoyi. (2020). Research on visual vehicle detection and tracking based on deep learning. IOP Conference Series: Materials Science and Engineering. 892. 012051. 10.1088/1757-899X/892/1/ 012051.

Amara, Dinesh & Karthika, R. & Soman, K.. (2020). DeepTrackNet: Camera Based End to End Deep Learning Framework for Real Time Detection, Localization and Tracking for Autonomous Vehicles. 10.1007/978-3-030-30465-2_34.

Zaman, Mostafa & Saha, Sujay & Zohrabi, Nasibeh & Abdelwahed, Sherif. (2023). Deep Learning Approaches for Vehicle and Pedestrian Detection in Adverse Weather. 10.1109/ITEC55900.2023. 10187020.

Gao, Hongbo & Su, Huiping & He, Xi & liao, yanzhen & Wu, Yulin & Juping, Zhu & Zhang, Fei. (2023). Multi-target Detection and Classification for Intelligent Vehicle Based on Deep Learning. 10.1007/978-981-99-2789-0_29.

Lin, Lixiong & He, Hongqin & Xu, Zhiping & Wu, Dongjie. (2023). Realtime Vehicle Tracking Method Based on YOLOv5 + DeepSORT. Computational Intelligence and Neuroscience. 2023. 10.1155/2023/7974201.

Downloads

Published

24.03.2024

How to Cite

Ankireddy, P. ., Gopalakrishnan, S. ., & Reddy, V. L. . (2024). Automatic Vehicle Detection and Tracking Strategy Using Deep Learning Model (YOLO v2 & R-CNN). International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 338–343. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5145

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.