An Automated Vehicle Counting System Based on Blob Analysis for Traffic Surveillance

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, we propose a traffic surveillance system for vehicle counting. The proposed algorithm is composed of five steps: background subtraction, blob detection, blob analysis, blob tracking and vehicle counting. A vehicle is modeled as a rectangular patch and classified via blob analysis. By analyzing the blob of vehicles, the meaningful features are extracted. Tracking moving targets is achieved by comparing the extracted features and measuring the minimal distance between consecutive frame. The experimental results show that the proposed system can provide real-time and useful information for traffic surveillance.