Vehicle Counting System using Deep Learning and Multi-Object Tracking Methods
Haoxiang Liang, Huansheng Song, Huaiyu Li, Zhe Dai
Abstract
Using deep learning technology and multi-object tracking method to count vehicles accurately in different traffic conditions is a hot research topic in the field of intelligent transportation. In this paper, first, a vehicle dataset from the perspective of highway surveillance cameras is constructed, and the vehicle detection model is obtained by training using the You Only Look Once (YOLO) version 3 network. Second, an improved multi-scale and multi-feature tracking algorithm based on a kernel correlation filter (KCF) algorithm is proposed to avoid the KCF extracting single features and single-scale defects. Combining the intersection over union (IoU) similarity measure and the row-column optimal association criterion proposed in this paper, matching strategy is used to process the vehicles that are not detected and wrongly detected, thereby obtaining complete vehicle trajectories. Finally, according to the trajectory of the vehicle, the traveling direction of the vehicle is automatically determined, and the setting position of the detecting line is automatically updated to obtain the vehicle count result accurately. Experiments were conducted in a variety of traffic scenes and compared with published data. The experimental results show that the proposed method achieves high accuracy of vehicle detection while maintaining accuracy and precision in tracking multiple objects, and obtains accurate vehicle counting results which can meet real-time processing requirements. The algorithm presented in this paper has practical application for vehicle counting in complex highway scenes.