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An Approach to Segment and Track-Based Pedestrian Detection from Four-Layer Laser Scanner Data

机译：四层激光扫描仪数据的段和基于轨道的行人检测方法

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摘要

Pedestrian detection is a critical perception task for autonomous driving and intelligent vehicle, and it is challenging due to the potential variation of appearance and pose of human beings as well as the partial occlusion. In this paper, we present a novel pedestrian detection method via four-layer laser scanner. The proposed approach deals with the occlusion problem by fusing the segment classification results with past knowledge integration from tracking process. First, raw point cloud is segmented into the clusters of independent objects. Then, three types of features are proposed to capture the comprehensive cues, and 18 effective features are extracted with the combination of the univariate feature selection algorithm and feature correlation analysis process. Next, based on the segment classification at individual frame, the track classification is conducted further for consecutive frames using particle filter and probability data association filter. Experimental results demonstrate that both back-propagation neural network and Adaboost classifiers based on 18 selected features have their own advantages at the segment classification stage in terms of pedestrian detection performance and computation time, and the track classification procedure can improve the detection performance particularly for partially occluded pedestrians in comparison with the single segment classification procedure.
机译：行人检测是自动驾驶和智能车辆的关键知识任务，由于人类外观和姿势的潜在变化以及部分闭塞是挑战。在本文中，我们通过四层激光扫描仪提出了一种新的行人检测方法。通过融合段分类结果与过去的知识集成从跟踪过程融合，提出的方法处理了遮挡问题。首先，原始点云被分段为独立对象的集群。然后，三种类型的特性，提出了以捕获全面线索，和18个有效特征与单变量特征选择算法和特征相关分析过程的组合提取。接下来，基于单个帧的段分类，使用粒子滤波器和概率数据关联滤波器进一步用于连续帧进行轨道分类。实验结果表明，基于18个所选特征的后传播神经网络和Adaboost分类器在行人检测性能和计算时间方面具有它们在段分类阶段的优点，并且轨道分类过程可以特别用于部分地提高检测性能与单段分类程序相比，闭塞行人。

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