| Proj No. | A3097-251 |
| Title | Comparison of AI/ML and Image processing algorithms for vehicle detection and classification for Smart land transportation system |
| Summary | Vehicle detection and classification are essential for modern transportation systems, enabling applications like traffic management, toll automation, and autonomous driving. The goal is to accurately detect vehicles in an image or video and classify them into different categories based on their type, size, or function. Students will compare traditional image processing-methods (edge detection, background subtraction, Haar Cascades, Histogram oriented gradients) and AL/ML methods (CNNs, YOLO, Faster R-CNN). Open-source datasets like Cityscapes, KITTI, or Berkeley DeepDrive and Real-world video footage will be used for analysis. Expected Outcomes will include a detailed comparison of AI/ML and image processing approaches for their accuracy, efficiency, and real-time performance and identification of the best approach for vehicle detection and classification for smart transportation applications. |
| Supervisor | A/P Mohammed Yakoob Siyal (Loc:S2 > S2 B2A > S2 B2A 28, Ext: +65 67904464) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Computer Engineering I (Loc: S2-B4c-15) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |