Patent ID: 8649594

Claim:
A surveillance system for classifying detected events into one or more event types and utilizing user feedback as to whether an event of the detected events is a true event to improve accuracy, comprising: a video analytics engine embodied on a computer processor configured to: receive video data from at least one video camera; generate an on-line data set from received video data, where the on-line data set comprises events and feature data; and utilize event classifiers as tuning parameters to classify events; a feedback collection engine embodied on a computer processor configured to present an event to a user and receive feedback from the user as to whether the event is a true event that matches an event type being monitored by the surveillance system; an active learning engine embodied on a computer processor configured to: generate event classifiers using an on-line feature data set and feedback received from a user, where the event classifiers can be used to classify detected events into one or more event types; apply an event classifier to an event and calculate a confidence score for the event representing the confidence of classifying a positive sample; normalize a plurality of confidence scores of event classifiers by mapping outputs of event classifiers to a common domain; duplicate the on-line feature data set to K groups with different partitions where each group includes a training set and a validation set with no overlap; perform iterations of ensemble classifier learning to train a classifier for each of the K groups, where each iteration comprises: training a classifier for each group with a classification error for each group returned from the previous iteration; computing a classification error for each of the K groups by applying the classifier trained for each group to the corresponding validation set within the group; aggregating the computed classification errors to obtain an overall error and determining if a learning stop criterion is satisfied by the overall error; stopping if the learning stop criterion is satisfied; and continuing to the next iteration if the learning stop criterion is not satisfied; and output K ensemble classifiers; and a surveillance system manager embodied on a computer processor configured to apply an event classifier to a second on-line data set generated by the video analytics engine.