Real-time vehicle detection using 3D lidar under complex urban environment
CHENG Jian1,2, XIANG Zhi-yu1,2,YU Hai-bin3, LIU Ji-lin1,2
1.Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; 2. Zhejiang Provincial Key Laboratory of Information Network Technology, Hangzhou 310027, China; 3. School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China
In order to improve the vehicle detection performance, a real-time algorithm to accurately detect static or dynamic vehicle in a single frame under complex urban environment was presented. The system combines 3D point clouds and 2D grid map, i.e., obstacle segmentation with contour extracting in 2D grid map and obstacle and vehicle detection in 3D point clouds. The detecting performance mainly depends on the designed features which should have obvious distinguishing characteristics. To solve the difficulties in distinguishing geometry similar objects and handle occlusion issues, three novel features, i.e., intensity reflection probability distribution, lengthways contour in height, and position-related features were proposed. Finally a support vector machine (SVM) classifier was used to realize real-time detection. The comparison of detecting performance from different features and combined features was analyzed as well. Experiments under real urban environment show that the algorithm can stably detect vehicles within 200 ms per frame, verified the robustness of the proposed method.
CHENG Jian, XIANG Zhi-yu,YU Hai-bin, LIU Ji-lin. Real-time vehicle detection using 3D lidar under complex urban environment. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2014, 48(12): 2101-2106.
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