Traffic Accident Risk Analysis Based on Relation of Common Route Models

Abstract

This paper proposes a novel accident prediction approach based on extracting the relation between interested vehicles and increasing risk factor according to anomaly detection in real time traffic videos. In learning process of the traffic model at intersections, we detect all trajectories by tracking of each vehicle and then group them considering road model. All trajectories are clustered by Continuous Hidden Markov Model with Mixture of Gaussian (MoG) and Common Route Model (CRM) for each group of trajectories is found. After extracting all CRM's and defining their relations, in real time traffic analysis process, partial motion of the vehicles are evaluated and anomalies are detected if there is. In this approach, while searching for accident risk, partial trajectories of vehicles are classified to the most similar CRM's. For each source vehicle, risk factors are calculated with target vehicles that are in related CRM's and has Region of Interest (ROI) intersected with source vehicle. The advantage of this approach is that the system does only analyze vehicles in accident risk and this increases the performance of the system. Beside these, since CRM information and their features like relations, directions and likelihood in classification process are learned, anomalies can easily be detected and used as risk enhancer. Experimental results show that the proposed model has high prediction rate in real world accident events.