Real time and robust intersection traffic flow measurement using traffic zones in a hybrid background modelling for smart cameras

Background modelling methods for identifying Vehicles in a traffic video surveillance is a fundamental task in computer-vision applications. In intelligent transportation systems (ITS), traffic parameters extraction at intersections is one of the critical and challenging tasks in urban traffic management. For intersection traffic analyzing where objects have different characteristics such as varying velocities, stop and go, it is necessary to use the adaptive background mixture model to learn background model faster and more accurately, instead of using single rate of adaptation, which is not adequate. The main focus of this research is to analyze activities at intersection for detecting and classifying vehicles and then extract traffic flow which assists in regulating traffic lights for using in a smart camera. Traffic zones definition in intersection video based on majority motions, greatly reduce the computations. A smart camera’s fundamental purpose is to analyze a scene and report statics and activities of interest which is not an image. Ground-truth experiments with urban traffic sequences show that our proposed algorithm is very promising relative to results using other techniques.

Keywords:

Background subtraction , Computer-vision , Intersection traffic flow , Traffic zone , Smart camera