1. A computer implemented method for ranking detected anomalies, the method comprising executing on a processor the steps of: generating a plurality of anomaly confidence decision values for image features extracted from a video data input from a camera through an image field of the camera as a function of fitting the extracted image features to normal patterns that are defined by dominant distributions of the extracted image features, and to anomaly patterns that are defined by rare distributions of the extracted image features; normalizing values of the image features extracted from the image field and associated with each of the plurality of anomaly confidence decision values; clustering the image feature values extracted from the image field and associated with each of the plurality of anomaly confidence decision values; determining spatial locations within the field of view of the clustered extracted image feature values of the anomaly confidence decision values that are correlated to features of interest of a real-world scene represented within the field of view; assigning a first weighting to a first anomaly of the anomaly confidence decision values that is higher than a second weighting assigned to a second anomaly of the anomaly confidence decision values in response to the determined spatial location of the clustered extracted image feature values of said first anomaly confidence decision value being within a portion of the field of view of the input video that is correlated with a cordoned off area of the real-world scene, wherein the determined spatial location of the clustered extracted image feature values of the second anomaly confidence decision value is outside the portion; and ranking the plurality of anomaly confidence decision values as a function of their respective assigned weightings.