Online detection of anomaly behaviors based on multidimensional trajectories

Abstract In the surveillance domain, timely detection of anomaly behaviors is very important and is a great challenge to human operators due to information overload, fatigue and inattention. Many anomaly detection algorithms based on trajectories have been proposed for this problem. However, these algorithms generally have problems such as complex parameter setting, unfaithful statistical model, not well-calibrated false alarm rate, poor ability of online learning and sequential anomaly detection, etc. The theory of conformal prediction was introduced to solve these problems by constructing the sequential Hausdorff nearest neighbor conformal anomaly detector. Yet, it only considers position information of the targets and is not sensitive to velocity and course anomaly behaviors. And the run times are increasing as the increase of the data size, which is not appropriate for early warning surveillance application. In order to solve these problems, sequential multi-factor Hausdorff nearest neighbor conformal anomaly detector (SMFHNN CAD) and sequential multi-factor Hausdorff nearest neighbor inductive conformal anomaly detector (SMFHNN ICAD) based on multidimensional trajectories are proposed in this paper. Experiments in both simulated military scenario and realistic civilian scenario show the presented algorithm has a good performance to online detect anomaly behaviors and would have a wide prospect in early warning surveillance systems.