A novel evidence based model for detecting dangerous situations in level crossing environments

This paper aims to develop a video surveillance system to detect, recognize and evaluate potentially dangerous situations in level crossing environments. First, a set of moving objects are detected and separated using an automatic clustering process coupled to an energy vector comparison strategy. Then, a multi-object tracking algorithm, based on optical flow propagation and Kalman filtering correction with adaptive parameters, is implemented. The next step consists on using a Hidden Markov Model to predict trajectories of the detected objects. Finally, the trajectories are analysed with a particular credibility model to evaluate dangerous situations at level crossings. Real data sets are used to test the effectiveness and robustness of the method. This work is developed within the framework of PANsafer project, supported by the French work program ANR.