Toward Visually Inferring the Underlying Causal Mechanism in a Traffic-Light-Controlled Crossroads

The analysis of the events taking place in a crossroads offers the opportunity to avoid harmful situations and the potential to increase traffic efficiency in modern urban areas. This paper presents an automatic visual system that reasons about the moving vehicles being observed and extracts high-level information, useful for traffic monitoring and detection of unusual activity. Initially, moving objects are detected using an adaptive background image model. Then, the vehicles are tracked down by an iterative method where the features being tracked are updated frame by frame. Next, paths are packed into routes using a similarity measure and a sequential clustering algorithm. Finally, the crossroads activity is organized into states representing the underlying mechanism that causes the type of motion being detected. We present the experimental evidence that suggests that the framework may prove to be useful as a tool to monitor traffic-light-controlled crossroads.