Abnormal behavior detection system and method by utilizing automatic multi-feature clustering method

A multi-feature and behavioral technology, applied in the field of abnormal behavior automatic detection system, can solve the problems of time-consuming and expensive maintenance, and achieve the effect of saving maintenance cost, saving construction cost, improving application range and practicability

Active Publication Date: 2012-03-21
GORILLA TECH
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  • Abnormal behavior detection system and method by utilizing automatic multi-feature clustering method
  • Abnormal behavior detection system and method by utilizing automatic multi-feature clustering method
  • Abnormal behavior detection system and method by utilizing automatic multi-feature clustering method

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[0037] In order to make the technical problems, technical solutions and advantages to be solved by the embodiments of the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0038] Please refer to figure 1 As shown, the architecture diagram of an embodiment of the abnormal behavior detection system using the multi-feature automatic clustering method provided by the present invention. The embodiment of the present invention includes a feature extraction unit 11 , a behavior model building unit 13 , a behavior judgment unit 15 , and an output unit 17 . The present invention particularly uses the feature extraction unit 11 to extract feature data with special meaning in the monitoring data 101 to form a feature group (group), including features related to one or more objects and environmental features that have nothing to do with the objects, using a behavior model The establishment unit 13 obtains the resul...

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Abstract

The invention provides an abnormal behavior detection system and a method by utilizing an automatic multi-feature clustering method; combine a feature group according to the obtained features of all monitoring data, for instance, multiple features are captured from a monitoring video; then build a normal behavior model and an abnormal behavior model after processing and analysis by a machine learning algorithm, and in one embodiment, the behaviors with low occurrence frequency are considered as the abnormal behavior. In the invention, supervised learning can also be adopted, the abnormal behavior model in the behavior models generated by automatic classification is defined artificially, so as to causing the abnormal behavior detection to be in accordance with the requirements of a user; and automatically classify into normal behaviors and abnormal behaviors according to the various captured features after the behavior model is built. In the invention, the system and the method are suitable for detecting various traffic abnormal behaviors such as traffic violation behaviors, for instance, running the red light, violation of right-lane driving and violation of right direction driving.

Description

technical field [0001] The present invention relates to an abnormal behavior automatic detection system and method using a multi-feature automatic clustering method, in particular to a method for detecting traffic abnormal behavior using an image analysis method. Background technique [0002] Abnormal behavior refers to irregular or rare behavior. The purpose of abnormal behavior detection is to detect objects such as people or vehicles, whether irregular or rare behavior occurs, such as pedestrians crossing the road illegally or vehicles violating traffic rules. Abnormal behavior detection is one of the main topics of current automatic video data retrieval and content analysis. It is used for abnormal intrusion detection, home elderly safety monitoring, neighborhood alley safety monitoring, etc. [0003] In the current known technologies, abnormal behavior detection mostly uses a single characteristic feature as the basis for identifying whether it is an abnormal behavior. ...

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Application Information

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IPC IPC(8): G06K9/62H04N7/18
Inventor 倪嗣尧蓝元宗林仲毅陈翊玮
Owner GORILLA TECH
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