A novel real-time analyzer for moving objects on freeway

This paper presents a real-time and cost effective method for sensing moving objects on the freeway. Based on the proposed method, we can evaluate the traffic rate of vehicles, the speed of moving objects and distinguish the kind of vehicle using their length. It is different from the conventional vehicle detection systems that need expensive hardware architecture, such as the multiprocessor system and VME bus, to implement the complicated algorithm. We develop an efficient and simple algorithm that has been successfully installed in a general industrial IBM-compatible PC. The two trigger lines policy is proposed to reduce the computation time of this system. Because there is an error signal due to the difference between the moving objects and that of the background on the trigger line, we construct a formal grammar to describe these pulse-like error signals based on the compiler´s technique in the software engineering. Therefore, the system states can be easily derived by the semantic routines. To overcome the brightness of day/night-switching and the cover of cloudy shadow, etc., we use a novel adaptive neural network to train the grey level of the background, and then the system parameters can be adapted quickly. The analyzer is called VSS (Vehicle Supervising System), and has been developed and tested on the freeway between Taipei and Lin-Ko City in Taiwan