Publication:
Local feature saliency classifier for real-time intrusion monitoring

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2014-07-28
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Society of Photo-Optical Instrumentation Engineers (SPIE)
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Abstract
We propose a texture saliency classifier to detect people in a video frame by identifying salient texture regions. The image is classified into foreground and background in real time. No temporal image information is used during the classification. The system is used for the task of detecting people entering a sterile zone, which is a common scenario for visual surveillance. Testing is performed on the Imagery Library for Intelligent Detection Systems sterile zone benchmark dataset of the United Kingdom's Home Office. The basic classifier is extended by fusing its output with simple motion information, which significantly outperforms standard motion tracking. A lower detection time can be achieved by combining texture classification with Kalman filtering. The fusion approach running at 10 fps gives the highest result of F1=0.92 for the 24-h test dataset. The paper concludes with a detailed analysis of the computation time required for the different parts of the algorithm.
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Texture saliency, Visual surveillance, People tracking, Clustering, Foreground classification, Sterile zone, Intrusion detection, Pedestrian tracking, Closed circuit television
Bibliographic citation
Buch, N. y Velastin, S.A. (2014). Local feature saliency classifier for real-time intrusion monitoring. Optical Engineering, 53(7), 073108.