An Integrated Approach for Multi-Object Detection and Tracking in Traffic Monitoring Using YOLOv9c and ByteTrack - Nguyen Thi Thanh Thuy; Ho Van Luc; Nguyen Thi Thai An; Phung The Bao; Nguyen Thi Dinh (Journal of Technical Education Science, 2026)
This paper proposes an integrated method for object detection and tracking in congested traffic environments, based on a combination of the YOLOv9c object detection model and the ByteTrack multi-object tracking algorithm. In this proposed method, the YOLOv9c model is trained and fine-tuned to enhance the performance of vehicle detection in complex conditions. Simultaneously, ByteTrack algorithm links objects across extracted video frames by leveraging both high- and low-confidence bounding boxes. This approach reduces the identity loss and increases the stability of object tracking in traffic, especially in conditions with high object density and severe occlusion. To implement this method, the object detection model was trained and refined on the BDD100K dataset, combined with the Vietnam Traffic Dataset, with a focus on common vehicle classes, including bicycles, motorbikes, cars, buses, and trucks. Experimental results showed that the model achieved a Precision of 89.8% and a Recall of 72.7% in the daytime traffic congestion scenario, and a recall rate of 90.1% in nighttime conditions. For the multi-object tracking problem, the system achieved an IDF1 of 84.3%, demonstrating its ability to maintain stable object identification even in the presence of obstructions, and achieved an MOTA of 69.9% under favorable observation conditions. These results confirm that the proposed method is highly effective in detecting and tracking traffic objects and has potential applications in intelligent traffic monitoring systems and real-time video analysis.