Abnormal behavior detection system and method using automatic classification of multiple features
First Claim
1. A non-transitory computer storage medium storing a behavior detection program using an automatic classification of multiple features, comprising:
- extracting a set of features associated with each of a plurality of objects respectively from monitoring data;
establishing at least one behavioral model for classifying at least one behavior type of objects after a cluster analysis based on similarities among the separated sets of features;
determining a behavior type of an object according to an extracted set of features of said object and the at least one behavior model; and
outputting the determined behavior type of the said object.
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Abstract
Described herein are a system and a method for abnormal behavior detection using automatic classification of multiple features. Features from various sources, including those extracted from camera input through digital image analysis, are used as input to machine learning algorithms. These algorithms group the features and produce models of normal and abnormal behaviors. Outlying behaviors, such as those identified by their lower frequency, are deemed abnormal. Human supervision may optionally be employed to ensure the accuracy of the models. Once created, these models can be used to automatically classify features as normal or abnormal. This invention is suitable for use in the automatic detection of abnormal traffic behavior such as running of red lights, driving in the wrong lane, or driving against traffic regulations.
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Citations
17 Claims
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1. A non-transitory computer storage medium storing a behavior detection program using an automatic classification of multiple features, comprising:
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extracting a set of features associated with each of a plurality of objects respectively from monitoring data; establishing at least one behavioral model for classifying at least one behavior type of objects after a cluster analysis based on similarities among the separated sets of features; determining a behavior type of an object according to an extracted set of features of said object and the at least one behavior model; and outputting the determined behavior type of the said object. - View Dependent Claims (2, 3, 4, 5, 6, 7, 16)
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8. A behavior detection method which performs automatic classification based on multiple features, comprising:
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extracting a set of features associated with each of a plurality of objects respectively from the monitoring data; performing a cluster analysis based on similarities among the separated sets of features; establishing at least one behavior model for classifying at least one behavior type of objects based on the result of the cluster analysis; determining a behavior type of an object according to an extracted set of features of said object and the at least one behavior model; and outputting the determined behavior of the said object. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 17)
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Specification