Detection of anomalous driving behaviors by unsupervised learning of graphs
Luc Brun, Benito Cappellania, Alessia Saggese, Mario Vento · 2014
In this paper we propose a graph based approach for detecting abnormal behaviors starting from the analysis of vehicles' trajectories. The scene is partitioned into zones and is dynamically represented as a graph by evaluating the distribution of trajectories belonging to the training set. Furthermore, four different strategies are proposed in order to verify if a test trajectory belongs to the scene and then can be considered normal by evaluating the probability that this trajectory belongs to the graph. Our algorithms have been tested on the standard MIT Trajectories dataset and the obtained results confirm the effectiveness of the proposed approach.