Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks

This paper presents a novel real-time pedestrian detection system utilizing a LIDAR-based object detector and convolutional neural network (CNN)-based image classifier. Our method achieves over 10 frames/second processing speed by constraining the search space using the range information from the LIDAR. The image region candidates detected by the LIDAR are confirmed for the presence of pedestrians by a convolutional neural network classifier. Our CNN classifier achieves high accuracy at a low computational cost thanks to its ability to automatically learn a small number of highly discriminating features. The focus of this paper is the evaluation of the effect of region of interest (ROI) detection on system accuracy and processing speed. The evaluation results indicate that the use of the LIDAR-based ROI detector can reduce the number of false positives by a factor of 2 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 per second.