| Proj No. | A3098-251 |
| Title | AI/ML and Image processing algorithms for vehicle Queue Length Estimation & Congestion Monitoring for Smart land transportation system |
| Summary | The goal of this project is to estimate the length of a queue of vehicles at traffic signals, toll booths, or congested areas and use this information for real-time congestion monitoring. This can help optimize traffic light timing, reduce waiting times, and improve urban mobility. Traditional Image Processing-Based Approaches will include Edge Detection & Contour Analysis, Morphological Operations, Background Subtraction, which will track vehicle motion to determine if vehicles are stationary and then detect the length of the queue. AI/ML-Based Approaches will include YOLO, Faster R-CNN, or SSD to detect and predict the length of the queue. Expected output will include status of the traffic based on the length of the queue, such as light, normal, or heavy traffic |
| Supervisor | A/P Mohammed Yakoob Siyal (Loc:S2 > S2 B2A > S2 B2A 28, Ext: +65 67904464) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Computer Engineering II (Loc: S2-B3b-08) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |