An Implementation of Moving Object Detection, Tracking and Counting Objects for Traffic Surveillance System

Moving vehicle detection in digital image sequences is one of the key technologies of Intelligent Transportation Systems (ITS). Traffic Surveillance System is being more and important with the enlarging of urban scale and increasing number of vehicles. This Paper presents an intelligent vehicle counting method based on blob analysis in traffic surveillance. The algorithm is composed of moving object segmentation, blob analysis, and tracking. By analyzing the blob of vehicles, the meaningful features are extracted. In addition, the speed 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 useful information for traffic surveillance. We analyze the procedure of video-based traffic congestion system and divide it into graying, binarization, denoising and moving target detection. The system first reads video and converts them into grayscale images. We also put forward a Boundary block detection algorithm with noise reduction to identify the moving objects.