Real time classification and tracking of multiple vehicles in highways

Real time road traffic monitoring is one of the challenging problems in machine vision, especially when one is using commercially available PCs as the main processor. In this paper, we describe a real-time method for extracting a few traffic parameters in highways such as, lane change detection, vehicle classification and vehicle counting. In addition, we will explain a real time method for multiple vehicles tracking that has the capability of occlusion detection. Our tracing algorithm uses Kalman filter and background differencing techniques. We used morphological operations for vehicle contour extraction and its recognition. Our algorithm has three phases, detection of pixels on moving objects, detection of a “Shape of Interest” in frame sequences and finally determination of relation among objects also in frame sequences. Our system is implemented on a PC with Pentium II 800 MHZ CPU. Its processing speed was measured to be 11 frames per second. The accuracy of measurement was 96%.