Intelligent Vehicle Counting Method Based on Blob Analysis in Traffic Surveillance

This paper presents an intelligent vehicle counting method based on blob analysis in traffic surveillance. The proposed algorithm is composed of three steps: Processing is done by three main steps: moving object segmentation, blob analysis, and tracking. 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 two temporal images. In addition, the velocity of each vehicle and the vehicle flow through a predefined area can be calculated by analyzing blobs of vehicles. The experimental results show that the proposed system can provide real-time and useful information for traffic surveillance.