*3.1.2 Analysis and relaxation of ideal tracking assumptions - experimentation with noisy data*

Most of the *false negatives* are abnormal trajectories that cannot easily be discriminated from the normal even by a human operator. Soft thresholding could be used in order to raise alerts for a human supervisor. On the other hand, the main reason for the *false positives* is the fact that airport travelers chose to move in ways that may not necessarily be similar to the normal trajectories. Large airport congestions make the aforementioned phenomenon even more intense.

Although the conditions the risk assessment algorithm was evaluated under assumed perfect knowledge of the traveler trajectories, relaxation of the assumption of perfect knowledge of the traveler trajectories by injecting noise in the position accuracy and/or assuming missing position data, did not have a considerable negative effect on the detection of abnormal trajectories as discussed next.

In order to assess the performance of the anomaly detection algorithm in realistic conditions we introduce noise in the data to emulate the uncertainty in passengers' positions reports. The "noisy data" emulate the inaccuracy in the reports of the *Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture DOI: http://dx.doi.org/10.5772/intechopen.96209*
