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Exemplar-based background model initialization
Proceedings of the third ACM international workshop on Video surveillance & sensor networks, 2005Most of the automated video-surveillance applications are based on background (BG) subtraction techniques, that aim at distinguishing moving objects in a static scene. These strategies strongly depend on the BG model, that has to be initialized and updated. A good initialization is crucial for the successive processing.
COLOMBARI, ANDREA +3 more
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SPIE Proceedings, 2007
An adaptive background model aiming at outdoor vehicle detection is presented in this paper. This model is an improved model of PICA (pixel intensity classification algorithm), it classifies pixels into K-distributions by color similarity, and then a hypothesis that the background pixel color appears in image sequence with a high frequency is used to
Xiaochun Lu +3 more
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An adaptive background model aiming at outdoor vehicle detection is presented in this paper. This model is an improved model of PICA (pixel intensity classification algorithm), it classifies pixels into K-distributions by color similarity, and then a hypothesis that the background pixel color appears in image sequence with a high frequency is used to
Xiaochun Lu +3 more
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A hybrid codebook background model for background subtraction
2011 IEEE Workshop on Signal Processing Systems (SiPS), 2011Real-time segmentation of scene into foreground and background is an important issue for many applications. Different from previous codebook (CB) methods, this paper introduces a hybrid CB model by combining the mixture of Gaussian (MOG) method and the CB method.
I-Ting Sun +2 more
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Thermal background model studies
Infrared Physics & Technology, 1995Abstract A theoretical model for calculating the thermal IR radiation of different backgrounds was developed. The model framework consists of a system of steady-state energy budget equations describing the energy interaction which takes place within the underlying scene. Knowing the thermal characteristics and the driving meteorological variables, we
Jian-Qi Zhang +4 more
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