Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks.

Author(s)
Sakai, U. & Szarvas, M.
Year
Abstract

This paper presents a pedestrian detection system based on sensor fusion of LIDAR and convolutional neural network based image classifier. This method achieves over 10 frames/second processing speed by utilizing the LIDAR. The focus of this paper is the evaluation of the effect of fusing the LIDAR compared to the image-only system. The evaluation results indicate that fusing the LIDAR can reduce the number of false positives by a factor of2 and reduce the processing time by a factor of 4. The single frame detection accuracy of the system is above 90% when there is 1 false positive/second. For the covering abstract see E134653.

Request publication

8 + 1 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.

Publication

Library number
C 41376 (In: C 40997 CD-ROM) /70 / ITRD E136096
Source

In: Proceedings of the 13th World Congress and Exhibition on Intelligent Transport Systems (ITS) and Services, London, United Kingdom, 8-12 October 2006, 8 p.

Our collection

This publication is one of our other publications, and part of our extensive collection of road safety literature, that also includes the SWOV publications.