Tunnel Traffic Enforcement Using Visual Computing and Field-Programmable Gate Array-Based Vehicle Detection and Tracking †
Abstract
1. Introduction
2. Background Knowledge
2.1. Foreground Detection
2.2. Object Detection
2.3. Object Tracking
- Step 1:
- Obtain frame and the next frame .
- Step 2:
- Predict the next search window based on the current tracked object location box , where , , , and .
- Step 3:
- Detect ORB key points in and , and extract the corresponding binary descriptors and .
- Step 4:
- Match between and , then get two matched subsets of key points and .
- Step 5:
- Calculate the geometric centers of and , denoted as and .
- Step 6:
- Update the location based on the motion vector. If no match is found between and , use the last motion vector .
- Step 7:
- 1 and repeat Step 1.
2.4. FPGA
3. Proposed Architecture and Processing Procedures
3.1. Foreground Detection
3.2. Vehicle Detection
3.3. Vehicle Tracking
3.4. Road Marking Detection
3.5. Training Model
4. Results and Discussions
4.1. Hardware Acceleration
4.2. Performance Test
4.3. Traffic Enforcement
- Vehicle trajectory detection: After detecting vehicles in each frame, vehicle tracking is enabled by analyzing image similarity, distance, and size, assigning a unique ID to each vehicle. The coordinates in each frame are then used for trajectory analysis.
- Lane-switching recognition: By collecting images and filtering out variations, the background view is obtained from the camera’s perspective and analyzes the lane boundaries. With the integration of trajectory analysis, the system identifies lane-switching behaviors, including weaving or driving across lane markings.
- Illegal left and right turn detection: When the camera is positioned at an intersection, the system detects illegal turning behaviors through trajectory analysis and lane recognition.
- Traffic flow statistics: Whenever a new vehicle appears on the screen, it is counted, providing real-time statistics on the number of vehicles entering the camera’s field of view per minute, enabling congestion analysis and monitoring.
5. Conclusions
- Real-time traffic monitoring in tunnels to instantly monitor lane violations and notify law enforcement agencies for subsequent handling.
- Collision prevention in tunnels to immediately alert vehicles behind to reroute and avoid rear-end collisions and traffic congestion in case of an accident.
- Reduced system size and cost by minimizing the system architecture, improving computational speed, and reducing system development costs.
- Automatic adaptation: Since it does not require pre-training, the system automatically adjusts to the local environment and enhances its versatility and ease of use.
- Seamless integration with existing cameras: The system shares and analyzes footage from existing surveillance systems without new installations, reducing deployment costs and increasing coverage of the intelligent monitoring system on the roads.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Developed Method | YOLO-v2-Tiny with Hardware Acceleration | |
---|---|---|
Computing Speed | 480p@20fps | 480p@2fps |
RAM | <1 MB | ~127 Mb |
Training Data | 200 frames | ~3 1053 105 pictures |
Training Time | <10 s | N/A |
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Lin, Y.-C.; Lin, R.-S. Tunnel Traffic Enforcement Using Visual Computing and Field-Programmable Gate Array-Based Vehicle Detection and Tracking. Eng. Proc. 2025, 92, 30. https://doi.org/10.3390/engproc2025092030
Lin Y-C, Lin R-S. Tunnel Traffic Enforcement Using Visual Computing and Field-Programmable Gate Array-Based Vehicle Detection and Tracking. Engineering Proceedings. 2025; 92(1):30. https://doi.org/10.3390/engproc2025092030
Chicago/Turabian StyleLin, Yi-Chen, and Rey-Sern Lin. 2025. "Tunnel Traffic Enforcement Using Visual Computing and Field-Programmable Gate Array-Based Vehicle Detection and Tracking" Engineering Proceedings 92, no. 1: 30. https://doi.org/10.3390/engproc2025092030
APA StyleLin, Y.-C., & Lin, R.-S. (2025). Tunnel Traffic Enforcement Using Visual Computing and Field-Programmable Gate Array-Based Vehicle Detection and Tracking. Engineering Proceedings, 92(1), 30. https://doi.org/10.3390/engproc2025092030