Support vector machines for robust trajectory clustering
Many event analysis systems are based on the detection of uncommon feature patterns that could be associated to anomalous events; the uncommon patterns are identified by comparison with a "normality model" describing the previously acquired data. In this work we propose an anomaly detection system based on trajectory clustering with single-class support vector machines. However, SVM parameter tuning would require an a-priori estimate of the number of outlier trajectories in the training data, which is unknown. We here propose a technique for automatic estimation of the number of outliers, thus avoiding the arbitrary choice of constant tuning parameters.