1. A computer implemented method for detecting anomalies, the method comprising executing on a processor: tracking movement of an object through an image field of a camera, wherein the object is detected within a video data input from the camera, wherein the image field is partitioned into a matrix comprising a grid of a plurality of different local units, and wherein the tracking generates a trajectory of the object's motion that passes through a subset of the local units that is less than a totality of the plurality of the different local units; extracting image features from the video data with respect to only each of the subset of the local units of the plurality of local units; generating a plurality of anomaly confidence decision values for the extracted image features as a function of fitting the extracted image features to normal patterns of a plurality of local motion pattern models that are defined by dominant distributions of the extracted image features, and to anomaly patterns of the plurality of local motion pattern models 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; ranking the plurality of anomaly confidence decision values as a function of their respective assigned weightings; extracting trajectory features from the video data input relative to the trajectory of the tracked object; generating a global anomaly confidence decision value for the object trajectory as a function of fitting the extracted trajectory features to a normal learned motion trajectory model, wherein the global anomaly confidence decision value indicates a likelihood that the object trajectory is normal or anomalous; determining whether anomalies have occurred as a function of combining the generated global anomaly confidence decision value with the anomaly confidence decision values; and prioritizing anomaly determinations as a function of the ranking of the anomaly confidence decision values.