An automated system for the analysis of the status of road safety using neural networks

This paper presents a neural network based novel automated system that can analyze vehicle mounted video data for improving road safety. There are video data collection systems currently available although no tools exist which could be used to automatically analyze vehicle mounted video data and estimate future crash sites. The main aim of the research presented in this paper is to develop a technique to segment roadside data obtained from vehicle mounted video into regions of interest, classify roadside objects and estimate the risk factor based on roadside conditions and objects for various crashes. A clustering technique for segmentation of roadside frames into regions of interest and a neural network to classify the regions of interest into objects are investigated. The preliminary segmentation and classification results on a small dataset taken from Transport and Main Roads’ vehicle mounted video data collection are promising.