A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range DataReportar como inadecuado


A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data


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División de Sistemas en Ingeniería Electrónica DSIE, Universidad Politécnica de Cartagena, Campus Muralla del Mar, s-n, Cartagena 30202, Spain



These authors contributed equally to this work.





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Academic Editor: Felipe Jimenez

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%. View Full-Text

Keywords: pedestrian detection; 3D LIDAR sensor; machine vision and machine learning pedestrian detection; 3D LIDAR sensor; machine vision and machine learning





Autor: Pedro J. Navarro †,* , Carlos Fernández †, Raúl Borraz † and Diego Alonso †

Fuente: http://mdpi.com/



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