Local feature saliency classifier for real-time intrusion monitoring

Norbert Buch; Sergio  A. Velastin

doi:10.1117/1.OE.53.7.073108

28 July 2014 Local feature saliency classifier for real-time intrusion monitoring

Norbert Buch, Sergio A. Velastin

Author Affiliations +

Optical Engineering, Vol. 53, Issue 7, 073108 (July 2014). https://doi.org/10.1117/1.OE.53.7.073108

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.

Citation Download Citation

Norbert Buch and Sergio A. Velastin "Local feature saliency classifier for real-time intrusion monitoring," Optical Engineering 53(7), 073108 (28 July 2014). https://doi.org/10.1117/1.OE.53.7.073108

Published: 28 July 2014

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