BPCluster: An Anomaly Detection Algorithm for RFID Trajectory Based on Probability

Indoor public places are facing more and more security risks, and need to be monitored to find potential anomalies. Benefiting from the advantages of low cost and high privacy, RFID is widely used in indoor monitoring. At present, it has become a common solution to construct the RFID raw data into time sequence trajectory, and then perform preprocessing and cluster analysis. However, there are redundant and uncertain factors in the RFID raw data, which affect the efficiency of anomaly detection. In this paper, we propose BPCluster, a probabilistic-based RFID trajectory anomaly detection algorithm for indoor RFID trajectories. The algorithm incorporates a probabilistic trajectory model, which reduces the redundancy and uncertainty through the context information of trajectories, and then clusters trajectories by the improved LCS algorithm to find abnormal trajectories. Experiments show that BPCluster has better performance in effectiveness and environmental adaptability, and the average accuracy in various environments reaches 91%.