Crowd abnormal behavior detection based on motion similar entropy
It is an important research content of graphic processing in the field of intelligent video surveillance to detect abnormal events.An algorithm based on entropy of motion similarity (EMS) to detect abnormal behavior was proposed.Based on the optical flow algorithm,taking the bottom flow block as the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2017-05-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000−0801.2017117/ |
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| Summary: | It is an important research content of graphic processing in the field of intelligent video surveillance to detect abnormal events.An algorithm based on entropy of motion similarity (EMS) to detect abnormal behavior was proposed.Based on the optical flow algorithm,taking the bottom flow block as the basic unit to get the scene motion information,according to the concept of social network model,the construction scene of the motion network model (MNM) was proposed,the division of the scene particles motion similarity was completed,and the distribution EMS of MNM was calculated in the time domain.Finally,the obtained image entropy was compared with the reasonable threshold,to determine whether abnormal behavior occured.Experimental results indicate that the proposed algo-rithm can detect abnormal behavior effectively and show promising performance while comparing with the state of the art methods. |
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| ISSN: | 1000-0801 |