title: A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data.
abstract: This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).
authors.affiliation: División de Sistemas en Ingeniería Electrónica (DSIE), Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena 30202, Spain. pedroj.navarro@upct.es.
authors.forename: Pedro J
authors.initials: PJ
authors.lastname: Navarro
authors.affiliation: División de Sistemas en Ingeniería Electrónica (DSIE), Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena 30202, Spain. carlos.fernandez@upct.es.
authors.forename: Carlos
authors.initials: C
authors.lastname: Fernández
authors.affiliation: División de Sistemas en Ingeniería Electrónica (DSIE), Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena 30202, Spain. raul.borraz@upct.es.
authors.forename: Raúl
authors.initials: R
authors.lastname: Borraz
authors.affiliation: División de Sistemas en Ingeniería Electrónica (DSIE), Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena 30202, Spain. diego.alonso@upct.es.
authors.forename: Diego
authors.initials: D
authors.lastname: Alonso
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D000069550:Machine Learning; D000069636:Pedestrians; D060388:Support Vector Machine
keywords: 3D LIDAR sensor; machine vision and machine learning; pedestrian detection