Event Camera Point Cloud Feature Analysis and Shadow Removal for Road Traffic Sensing

Abstract

Event cameras have a wide range of applications in the field of traffic flow detection owing to their ability to accurately identify moving targets while being insensitive to stationary targets. In this study, an event camera is installed in a roadside scenario to collect the point cloud data of moving targets. For traffic identification, the geometrical, quantitative and Gaussian projection characteristics of the point clouds for motor/non-motor vehicles and pedestrians are extracted, and their feature distributions are analyzed. Furthermore, to address the problem of shadow noise caused by sunlight, a shadow removal method based on feature similarity is proposed considering the point cloud distribution characteristics. Experimental results show that there are significant differences in the length-width ratio, pixel points, horizontal and vertical projection features among different traffic targets specified in vehicles, non-motor vehicles and pedestrians. In addition, the proposed shadow removal scheme demonstrates high accuracy of 96.5%, and the standard deviation is 1.3%.