Abstract:Urban road vehicle detection is an important part of modern intelligent transportation system-ITS. According to image sequence time-space relations of continuous change, the underneath shadow feature vehicle detection algorithm is further improved by constructing the time-space model of video image sequence, and is combined with the AdaBoost algorithm, filtering out false candidate region of vehicle. Experimental results demonstrate that the accuracy rate of proposed algorithm is 92.1%, with the false positive being 4.3%, the resolution of image being 1920*1088 and the time of processing being 76ms under the complex urban traffic environment. The algorithm effectively improves the high false detection rate and low effectiveness of AdaBoost and underneath shadow feature detection algorithms, and can meet the accuracy and robustness requirements of vehicle detection in the urban road environment.