Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach
DOI:
https://doi.org/10.56294/dm2024295Keywords:
Multi Detecting Object Tracking, Convolution Neural Network (CNN), Deep Learning, Traffic Detection, Machine Learning, Image ClassificationAbstract
Vehicle detection is an essential technology for intelligent transportation systems and autonomous vehicles. Reliable real-time detection allows for traffic monitoring, safety enhancements and navigation aids. However, vehicle detection is a challenging computer vision task, especially in complex urban settings. Traditional methods using hand-crafted features like HAAR cascades have limitations. Recent deep learning advances have enabled convolutional neural networks (CNNs) like Faster R-CNN, SSD and YOLO to be applied to vehicle detection with significantly improved accuracy. But each technique has tradeoffs between precision and processing speed. Two-stage detectors like Faster R-CNN are highly accurate but slow at 7 FPS. Single-shot detectors like SSD are faster at 22 FPS but less precise. YOLO is extremely fast at 45 FPS but has lower accuracy. This paper reviews prominent deep learning vehicle detectors. It proposes a new integrated method combining YOLOv3 detection, optical flow tracking and trajectory analysis to enhance both accuracy and speed. Results on highway and urban datasets show improved precision, recall and F1 scores compared to YOLOv3 alone. Optical flow helps filter noise and recover missed detections. Trajectory analysis enables consistent object IDs across frames. Compared to other CNN models, the proposed technique achieves a better balance of real-time performance and accuracy. Occlusion handling and small object detection remain open challenges. In summary, deep learning has enabled major progress but enhancements in model architecture, training data and occlusion handling are needed to realize the full potential for traffic management applications. The integrated method proposed offers improved performance over baseline detectors. We have achieved 99 % accuracy in our project
References
1. Gomaa, A., Minematsu, T., Abdelwahab, M., Abo-Zahhad, M., & Taniguchi, R. (2022). Faster CNN-based vehicle detection and counting strategy for fixed camera scenes. Retrieved from link. DOI: 10.1007/s11042-022-12370-9.
2. Ghahremannezhad, H., Shi, H., & Liu, C. (2022). Real-Time Accident Detection in Traffic Surveillance Using Deep Learning. Retrieved from link. DOI: 10.1109/IST55454.2022.9827736.
3. Arnold, M., Hoyer, M., & Keller, S. (n.d.). Convolutional Neural Networks for Detecting Bridge Crossing Events with Ground-Based Interferometric Radar Data. Retrieved from link. DOI: 10.5194/isprs-annals-v-1-2021-31-2021.
4. Mane, D., Sangve, S., Kandhare, S., Mohole, S., Sonar, S., & Tupare, S. (2023). Real-Time Vehicle Accident Recognition from Traffic Video Surveillance using YOLOV8 and OpenCV. Retrieved from link. DOI: 10.17762/ijritcc.v11i5s.6651.
5. Khoshboresh-Masouleh, M., & Shah-Hosseini, R. (2022). SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep metalearning. Retrieved from link. DOI: 10.5194/isprs-archives-XLVI-M-2-2022-141-2022.
6. Bharathi, G., & Anandharaj, G. (n.d.). A Conceptual Real-Time Deep Learning Approach for Object Detection, Tracking and Monitoring Social Distance using Yolov5. Retrieved from link. DOI: 10.17485/ijst/v15i47.1880.
7. Fernández, J., Cañas, J., Fernández, V., & Paniego, S. (2021). Robust Real-Time Traffic Surveillance with Deep Learning. Retrieved from link. DOI: 10.1155/2021/4632353.
8. Chaganti, R., Mourade, A., Ravi, V., Vemprala, N., Dua, A., & Bhushan, B. (2022). A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things. Retrieved from link. DOI: 10.3390/su141912828.
9. Bourja, O., Derrouz, H., Abdelali, H. A., Maach, A., Thami, R., & Bourzeix, F. (n.d.). Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3. Retrieved from link. DOI: 10.14569/ijacsa.2021.01208101.
10. Ji, Z., Gong, J., & Feng, J. (n.d.). A Novel Deep Learning Approach for Anomaly Detection of Time Series Data. Retrieved from link. DOI: 10.1155/2021/6636270.
Published
Issue
Section
License
Copyright (c) 2024 Patakamudi Swathi, Dara Sai Tejaswi, Mohammad Amanulla Khan, Miriyala Saishree , Venu Babu Rachapudi, Dinesh Kumar Anguraj (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
