The project introduces a Multi-Stage Traffic Anomaly Analysis Framework for identifying and analyzing urban traffic congestion, particularly in Dhaka. Originally utilizing Faster R-CNN and DBSCAN, it has been upgraded to state-of-the-art YOLOv9e and YOLOv10l models for enhanced accuracy and efficiency.
computer-vision deep-learning traffic-analysis faster-rcnn object-detection vehicle-detection dhaka dbscan-clustering pedestrian-detection explainable-ai traffic-congestion congestion-analysis eigen-cam yolov9 yolov10 traffic-anomaly traffic-congestion-analysis congested-region-detection
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Feb 5, 2025 - Jupyter Notebook