Switching Kalman Filter-Based Approach for Tracking and Event Detection at Traffic Intersections

Automatic event detection from video sequences has applications in several areas such as automatic visual surveillance, traffic monitoring for intelligent transportation systems, key frame detection for video compression, and virtual reality applications. In this work, we present a computer vision-based approach for event detection and data collection at traffic intersections. Specifically, we make the following two contributions: (i) a robust tracking algorithm for targets through combination of multiple cues and multiple motion models, and (ii) a simple event detection system using the results of a switching Kalman filter in combination with some simple rules. We show the results of tracking and event detection, such as, turning, stopped or stalling vehicles, as well as motion statistics (average speeds and accelerations), collected for some outdoor traffic scenes