Abnormal Trajectory Detection for Security Infrastructure

In this work, an approach for the automatic analysis of people trajectories is presented, using a multi-camera and card reader system. Data is first extracted from surveillance cameras and card readers to create trajectories which are sequences of paths and activities. A distance model is proposed to compare sequences and calculate similarities. The popular unsupervised model One-Class Support Vector Machine (One-Class SVM) is used to train a detector. The proposed method classifies trajectories as normal or abnormal and can be used in two modes: off-line and real-time. Experiments are based on data simulation corresponding to an attack scenario proposed by a security expert. Results show that the proposed method successfully detects the abnormal sequences in the scenario with very low false alarm rate.