Title :
Background subtraction method using codebook-GMM model
Author :
SeungJong Noh ; Deayoung Shim ; Moongu Jeon
Author_Institution :
Sch. of Inf. & Commun., Gwangju Inst. of Sci. & Technol., Gwangju, South Korea
Abstract :
In this paper, we present a new practical background subtraction method taking advantages of the conventional codebook and GMM-based approaches. The fundamental idea is approximating GMM parameters based on color statistics of background pixels which are clustered by the computationally efficient codebook scheme. The experiments on real visual surveillance dataset demonstrate that the performance of the proposed method is excellent in the aspects of subtraction accuracy and processing time.
Keywords :
Gaussian processes; image coding; image colour analysis; image motion analysis; object detection; GMM parameter; Gaussian mixture model; background pixel; background subtraction method; codebook scheme; codebook-GMM model; color statistics; processing time aspect; subtraction accuracy aspect; visual surveillance dataset; Color; Computational modeling; Mathematical model; Object detection; Probabilistic logic; Surveillance; Vectors;
Conference_Titel :
Control, Automation and Information Sciences (ICCAIS), 2014 International Conference on
Conference_Location :
Gwangju
DOI :
10.1109/ICCAIS.2014.7020540