Active and adaptive intelligent video surveillance system
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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Abstract
A method for assessing events detected by a surveillance system includes assessing the likelihood that the events correspond to events being monitored from feedback in response to a condition set by a user. Classifiers are created for the events from the feedback. The classifiers are applied to allow the surveillance system improve its accuracy when processing new video data.
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Citations
22 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method 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 using a video surveillance system, the method comprising:
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receiving video data from at least one video camera; generating an on-line data set from received video data, where the on-line data set comprises events and feature data; utilizing event classifiers as tuning parameters to classify events; presenting an event to a user and receiving feedback from the user as to whether the event is a true event that matches an event type being monitored by the surveillance system; generating 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; applying an event classifier to an event and calculating a confidence score for the event representing the confidence of classifying a positive sample; normalizing a plurality of confidence scores of event classifiers by mapping outputs of event classifiers to a common domain; duplicating 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; performing 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; outputting K ensemble classifiers; and applying an event classifier to a second on-line data set generated by the video analytics engine. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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Specification