V11-32

    Systems employed during surveillance activities might be enhanced using anomaly detection capabilities. Such capabilities can be employed to highlight situations, events, or objects that need an operator's attention, thus reducing their cognitive load and reaction time. Early detection of such situations provides critical time to take appropriate action, possibly before potential problems occur. However, the detection of conflict situations or general anomalous behavior in surveillance data is a complex analytical task that normally cannot be solved using purely visual analysis or purely automatic computational methods. On the one hand, the success of purely visual analysis methods for area surveillance often depends on factors such as the amount of sensor data that needs to be monitored, time constraints, and even operators' cognitive load and level of fatigue. On the other hand, current automatic anomaly detection solutions normally present high false alarm rates when dealing with complex situations. The high number of false alarms can become a nuisance for operators, who might react by turning anomaly detection capabilities off. Some researchers dispute the use of fully autonomous discovery systems in real-world settings, highlighting the need to include human knowledge in the discovery process.

     Most of the published work on anomaly detection focuses on the technological aspects: new methods, combinations of methods, additional improvements of existing methods, reduction of false alarms, correlations among alarms, etc. Publications regarding the use of anomaly detection methods in real environments or human factors studies regarding anomaly detection are scarce. In order to find optimal combinations of human expert knowledge and computational methods for anomaly detection, it is important to investigate how the surveillance of sea areas is carried out. This domain is suitable for studying the optimal combinations of expert knowledge and computational methods since it fulfills the characteristics of many data-intensive domains: large amounts of multivariate data, the need for operator support to solve complex problems, and the need for situation awareness to promote effective decision-making. Knowledge of how the analysis of traffic data is carried out on a daily basis can be used to propose ways to support such processes using data mining and visualization methods.


The passage implies which of the following?

A. The main drawback of autonomous anomaly discovery systems is that they can never achieve zero false alarm rates.
B. Further research in sea traffic movement will definitively determine the best possible combination of human expert knowledge and computational methods.
C. Research in surveillance systems has not been properly carried out because of the difficulty of analyzing the huge amount of data associated with the domain.
D. Some researchers believe that fully automatic surveillance methods cannot practically eliminate the requirement of human intervention.
E. No research work has been done on the non-technological aspect of the anomaly detection system.