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Detection of moving objects: The first stage of an autonomous surveillance system

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dc.contributor.author Keaikitse, M
dc.contributor.author Brink, W
dc.contributor.author Govender, Natasha
dc.date.accessioned 2012-10-30T13:21:45Z
dc.date.available 2012-10-30T13:21:45Z
dc.date.issued 2012-10
dc.identifier.citation Keaikitse, M., Brink, W. and Govender, N. Detection of moving objects: The first stage of an autonomous surveillance system. 4th CSIR Biennial Conference: Real problems relevant solutions, CSIR, Pretoria, 9-10 October 2012 en_US
dc.identifier.uri http://hdl.handle.net/10204/6249
dc.description 4th CSIR Biennial Conference: Real problems relevant solutions, CSIR, Pretoria, 9-10 October 2012 en_US
dc.description.abstract Object detection is an essential first stage in a surveillance system, primarily because it focuses all the subsequent processes. The standard approach to object detection is background subtraction. At the core of background subtraction is a module that maintains an image that is representative of the scene monitored by a camera. This work compares two background subbtraction/maintenance algorithms: adaptive Gaussian mixture model and the Wallflower method. The algorithms are evaluated using video footage of the real world. The Receiver Operating Characteristic (ROC) curves are used to quantify the performance of the algorithms. In our experiments, the adaptive Gaussian mixture model outperforms the Wallflower method. en_US
dc.language.iso en en_US
dc.subject Object detection en_US
dc.subject Background subtraction en_US
dc.subject Receiver Operating Characteristic en_US
dc.subject ROC en_US
dc.subject Video surveillance systems en_US
dc.subject Wallflower algorithm en_US
dc.subject Adaptive Gaussian mixture model en_US
dc.title Detection of moving objects: The first stage of an autonomous surveillance system en_US
dc.type Conference Presentation en_US


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