EFFICIENT RETRIEVAL OF ANOMALOUS EVENTS WITH PRIORITY LEARNING

摘要

Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures to fit to applied anomaly detection module local models. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies.

主权项

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.