Moving Object Classification Using 3D Point Cloud in Urban Traffic Environment

Moving object classification is essential for autonomous vehicle to complete high-level tasks like scene understanding and motion planning. In this paper, the authors propose a novel approach for classifying moving objects into four classes of interest using 3D point cloud in urban traffic environment. Unlike most existing work on object recognition which involves dense point cloud, this approach combines extensive feature extraction with the multiframe classification optimization to solve the classification task when partial occlusion occurs. First, the point cloud of moving object is segmented by a data preprocessing procedure. Then, the efficient features are selected via Gini index criterion applied to the extended feature set. Next, Bayes Decision Theory (BDT) is employed to incorporate the preliminary results from posterior probability Support Vector Machine (SVM) classifier at consecutive frames. The point cloud data acquired from the authors’ own LIDAR as well as public KITTI dataset is used to validate the proposed moving object classification method in the experiments. The results show that the proposed SVM-BDT classifier based on 18 selected features can effectively recognize the moving objects.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01737882
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 27 2020 9:07AM