Pedestrian Detection and Tracking System Based on Deep-SORT, YOLOv5, and New Data Association Metrics

Pedestrian tracking and detection have become critical aspects of advanced driver assistance systems (ADASs), due to their academic and commercial potential. Their objective is to locate various pedestrians in videos and assign them unique identities. The data association task is problematic, particularly when dealing with inter-pedestrian occlusion. This occurs when multiple pedestrians cross paths or move too close together, making it difficult for the system to identify and track individual pedestrians. Inaccurate tracking can lead to false alarms, missed detections, and incorrect decisions. To overcome this challenge, our paper focuses on improving data association in our pedestrian detection system's Deep-SORT tracking algorithm, which is solved as a linear optimization problem using a newly generated cost matrix. We introduce a set of new data association cost matrices that rely on metrics such as intersections, distances, and bounding boxes. To evaluate trackers in real time, we use YOLOv5 to identify pedestrians in images. We also perform experimental evaluations on the Multiple Object Tracking 17 (MOT17) challenge dataset. The proposed cost matrices demonstrate promising results, showing an improvement in most MOT performance metrics compared to the default intersection over union (IOU) data association cost matrix.

Automated summary

Pedestrian tracking and detection are important for advanced driver assistance systems (ADASs) due to their academic and commercial potential. Our paper focuses on improving data association in our pedestrian detection system's Deep-SORT tracking algorithm, utilizing new data association cost matrices based on metrics such as intersections, distances, and bounding boxes. Experimental evaluations show promising results with improved MOT performance metrics compared to the default intersection over union (IOU) data association cost matrix.