The surveillance of large sea, air or land areas normally involves the analysis of large volumes of heterogeneous data from multiple sources. Timely detection and identification of anomalous behavior or any threat activity is an important objective for enabling homeland security. While it is worth acknowledging that many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems for area surveillance are rarely used in the real world. We argue that such capabilities and applications present two critical challenges: (1) they need to provide adequate user support and (2) they need to involve the user in the underlying detection process. In order to encourage the use of anomaly detection capabilities in surveillance systems, this paper analyzes the challenges that existing anomaly detection and behavioral analysis approaches present regarding their use and maintenance by users. We analyze input parameters, detection process, model representation and outcomes. We discuss the role of visualization and interaction in the anomaly detection process. Practical examples from our current research within the maritime domain illustrate key aspects presented.
