Real-time Vehicle Detection and Tracking using YOLO-based Deep Sort Model: A Computer Vision Application for Traffic Surveillance

Abstract

Intelligent vehicle detection and counting are becoming increasingly important in the field of highway management. However, due to the different sizes of vehicles, their detection remains a challenge that directly affects the accuracy of vehicle counts. To address this issue, this paper proposes a vision-based vehicle detection and counting system using You Only Look Once (YOLO-V4) based DeepSORT model for real time vehicle detection and tracking from video sequences. Deep learning based Simple Real time Tracker (Deep SORT) algorithm is added, which will track actual presence of vehicles from video frame predicted by YOLO-V4 so the false prediction perform by YOLOV4 can be avoid by using DeepSort algorithm. The video will be converted into multiple frames and give as input to YOLO-V4 for vehicle detection. The detected vehicle frame will be further analysed by DeepSort algorithm to track vehicle and if vehicle tracked then DeepSort will put bounding box across tracked vehicle and increment the tracking count. The proposed model is trained with three different datasets such as public and custom collected dataset