A Survey on Traffic Sign Recognition, Pedestrian Detection Techniques, and Vehicle Detection Techniques for Autonomous Vehicles

Abstract

AbstractIn today’s world, autonomous vehicles are given more assiduity in comparison with the ones in previous years. The most significant feature which needs to be taken into rumination is the capability of an autonomous vehicle to effectively detect and recognize road and traffic signs from a certain meters and to detect the pedestrians on the road as well as to detect vehicles to prevent accidents. The paper mainly focuses on the techniques we can use for vehicle detection, pedestrian detection, and traffic sign detection and recognition (TSDR) systems. There are some distinctive kinds of road and traffic signs like refreshment, loose gravel, right hairpin bend, left hairpin bend, staggered intersection, round about, hump or rough road, unguarded level crossing, and so on. Traffic sign detection and recognition system identifies the traffic sign on the basis of attributes like color, shape, texture, etc. To be able to detect the pedestrians effectively is challenging due to different body shapes, sizes, clothing’s, poses, and lightening conditions outside. The processing of automatic vehicle detection and recognition using video as input is the challenging part to accomplish. Object detection is the most crucial and complex problem in computer vision field. The aim of this paper is to read, understand, and analyze about traffic sign detection techniques, vehicle detection techniques, and pedestrian detection for autonomous vehicles using convolutional neural networks that classify road signs present in that image into various different types.KeywordsTraffic sign recognitionConvolutional neural networkHistogram of oriented gradients feature descriptorSupport vector machine classifierHungarian algorithmKalman filter