Time-Spatial Multiscale Net for Vehicle Counting and Traffic Volume Estimation

Abstract

Vehicle counting and traffic volume estimation on traffic videos are challenging tasks for traffic monitoring and management. Most previous methods are based on a process of vehicle detection and tracking or a process of time-spatial image (TSI)-based background subtraction, which suffer from occlusion and small size of vehicles. To overcome these difficulties, a time-spatial-based structure is proposed for vehicle counting and traffic volume estimation, without relying on tracking and TSI-based background subtraction. This structure has three main parts. First, a TSI-based density map generation model is proposed for generating density maps for TSIs, which makes it possible to automatically generate TSI training samples. Second, the time-spatial multiscale net (TM-Net) is proposed for estimating density map of TSI; with the stacked multiscale modules and the spatial attention module, the proposed TM-Net can partly overcome the difficulties brought from occlusion, small size, and deformation. Finally, a vehicle counting and traffic volume estimation model is designed for counting and volume estimation on the results of TM-Net. Experiments performed on the public UA-DETRAC data set show that the proposed TM-Net-based vehicle counting method outperforms the tested representative counting methods, and the proposed framework also can estimate traffic volume efficiently.