A Detection Method for Vehicles in Nighttime by Virtual-loop Sensors Based on Kinect Depth Data

Detection methods for vehicles based on video cameras have problems of low accuracy,poor robustness,and difficult to identify types of vehicles in nighttime situations.A method using virtual-loop sensors based on Kinect depth data is proposed for detecting vehicles in nighttime.Firstly,depth image from Kinect is pre-processed to derive the target Motion Depth Map (MDM) and the Hole Depth Map (HDM).Secondly,virtual-loop sensors are set on MDM and HDM respectively,and generate integral images to compute the one-dimensional motion signals.The motion signals from corresponding MDM and HDM are fused to formulate the description of vehicle motions,from which vehicles are detected and counted.Then geometric features of vehicles are extracted,and types of vehicles are recognized by using SVM.The results show that the proposed method can accurately detect and count vehicles in nighttime situations with recognition rates 99.75 % and 99.25 % for one-lane and two-lane scenarios respectively.Its classify accuracy is 99.80 % in terms of dis tinguish light and heavy vehicles.The average time of processing one image is only 7 ms.