Distributed System for Crossroads Traffic Surveillance with Prediction of Incidents

The development of traffic surveillance systems is one of the crucial tasks in intelligent urban surveillance. The visual tracking techniques become more complex with a high computational cost. At the same time, they provide the wide possibilities for motion estimation and prediction in cluttered video sequences. Our contribution is a reasonable application of fast motion estimation with additional using of the clustering procedure. Then the Kalman filter is applied for vehicles’ motion analysis, and the particle filter is used for analysis of pedestrians’ behavior assuming that pedestrians are the weakly predictable objects on the crossroads. Also the distributed surveillance system based on the cloud and fog technologies was designed to process large volumes of video information provided from several IP-cameras in a real-time mode, when six full frames per s are transmitted.