Abstract
Moving object classification is essential for autonomous vehicle to complete high-level tasks like scene understanding and motion planning. In this paper, we 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, our 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 our 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.
Highlights
Autonomous driving has become an increasingly popular domain for intelligent transportation system [1, 2]
In order to test the performance of the proposed moving object classification method, four categories of the point cloud samples including vehicle, pedestrian, bicycle, and crowd are collected using Velodyne HDL-64E LIDAR equipped on our autonomous vehicle (Figure 4). e videos generated by 3 external cameras on the autonomous vehicle are acquired synchronously to manually label the real categories for the samples
We propose an approach for moving object classification using 3D point cloud in urban traffic environment. is approach classifies moving objects into four classes, namely vehicle, pedestrian, bicycle, and crowd. e accurate modeling of moving object classification using 3D point cloud consists of several procedures that all affect the final classification results
Summary
Autonomous driving has become an increasingly popular domain for intelligent transportation system [1, 2]. Arras et al [13] defined 14 static features including roundness and compactness to train the pedestrian classifier based on the point cloud of the legs This method can generate good recognition result at indoor environment, it is not suitable for outdoor. Motivated by the abovementioned analysis, we proposed a LIDAR-based classification method in this paper for four categories of moving objects, namely, vehicle, pedestrian, bicycle, and crowd. Is process consists of ground segmentation, the clustering of nonground points, and moving object detection Both global- and layer-based features are extracted to describe the geometric characteristics, and Gini index criterion is utilized to select the effective features based on the category attributes of training samples.
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