SECURITY EVENT RECOGNITION FOR VISUAL SURVEILLANCE
With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework for security event analysis in survei...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-05-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-1-W1/19/2017/isprs-annals-IV-1-W1-19-2017.pdf |
| Summary: | With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video
and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework
for security event analysis in surveillance videos. First, convolutional neural network (CNN) framework is used to detect objects of
interest in the given videos. Second, the owners of the objects are recognized and monitored in real-time as well. If anyone moves any
object, this person will be verified whether he/she is its owner. If not, this event will be further analyzed and distinguished between two
different scenes: moving the object away or stealing it. To validate the proposed approach, a new video dataset consisting of various
scenarios is constructed for more complex tasks. For comparison purpose, the experiments are also carried out on the benchmark
databases related to the task on abandoned luggage detection. The experimental results show that the proposed approach outperforms
the state-of-the-art methods and effective in recognizing complex security events. |
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| ISSN: | 2194-9042 2194-9050 |