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A framework for lab-based real-time video analysis on distributed camera networks

Farhad Dadgostar, Abbas Bigdeli, Sandra Mau, Terence Smith, Brian Lovell
Queensland Research Laboratory
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC ’10

@inproceedings{dadgostar2010framework,

   title={A framework for lab-based real-time video analysis on distributed camera networks},

   author={Dadgostar, F. and Bigdeli, A. and Mau, S. and Smith, T. and Lovell, B.},

   booktitle={Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras},

   pages={238–242},

   year={2010},

   organization={ACM}

}

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In the field of video analytics for surveillance, the trend towards the use of multi-camera and high definition video is increasing. This poses significant technical challenges in terms of video transmission and real-time processing for surveillance analytics, such as people recognition and tracking. Currently, available solutions are typically proprietary commercial systems which are costly to purchase. These proprietary systems also do not facilitate research collaboration across members of the computer vision community. We propose a framework for video analytics research based only on open-source software which is collaborative, scalable, interoperable, and distributed. This framework was successfully applied to the task of face recognition on both live video feeds and video datasets.
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