An Automated Nighttime Vehicle Counting and Detection System for Traffic Surveillance

Abstract

Robust and reliable traffic surveillance system is an urgent need to improve traffic control and management. Vehicle flow detection appears to be an important part in surveillance system. The traffic flow shows the traffic state in fixed time interval and helps to manage and control especially when there's a traffic jam. In this paper presents an effective traffic surveillance system for detecting and tracking moving vehicles in various nighttime environments. The proposed algorithm is composed of four steps: headlight segmentation and detection, headlight pairing, vehicle tracking, vehicle counting and detection. First, a fast segmentation process based on an adaptive threshold is applied to effectively extract bright objects of interest. The extracted bright objects are then processed by a spatial clustering and tracking procedure that locates and analyzes the spatial and temporal features of vehicle light patterns, and identifies and classifies moving cars and motorbikes in traffic scenes. The experimental results show that the proposed system can provide real-time and useful information for traffic surveillance.