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Active and adaptive intelligent video surveillance system

  • US 8,649,594 B1
  • Filed: 06/03/2010
  • Issued: 02/11/2014
  • Est. Priority Date: 06/04/2009
  • Status: Active Grant
First Claim
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1. 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.

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