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Panoramic Gaussian Mixture Model and large-scale range background substraction method for PTZ camera-based surveillance systems

Panoramic Gaussian Mixture Model and large-scale range background substraction method for PTZ camera-based surveillance systems,Kang Xue,Yue Liu,Gbola

Panoramic Gaussian Mixture Model and large-scale range background substraction method for PTZ camera-based surveillance systems  
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In this paper, we present a novel approach for con- structing a large-scale range panoramic background model that provides fast registration of the observed frame and local- izes the foreground targets with arbitrary camera direction and scale in a Pan-tilt-zoom (PTZ) camera-based surveil- lance system. Our method consists of three stages. (1) In the first stage, a panoramic Gaussian mixture model (PGMM) of the PTZ camera's field of view is generated off-line for later use in on-line foreground detection. (2) In the second stage, a multi-layered correspondence ensemble is generated off-line from frames captured at different scales which is used by the correspondence propagation method to register observed frames online to the PGMM. (3) In the third stage, foreground is detected and the PGMM is updated. The proposed method has the capacity to deal with the PTZ camera's ability to cover a wide field of view (FOV) and large-scale range. We dem- onstrate the advantages of the proposed PGMM background subtraction method by incorporating it with a tracking system for surveillance applications.
Published in 2012.
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