Object tracking with revised SMOG model

Huan Wang; Mingwu Ren; Jingyu Yang

doi:10.1117/12.748682

15 November 2007 Object tracking with revised SMOG model

Huan Wang, Mingwu Ren, Jingyu Yang

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880Y (2007) https://doi.org/10.1117/12.748682
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Spatial color Mixture Of Gaussians model (SMOG model) based similarity measure is superior to the popular color histogram based one since it considers not only the colors in a region, but also the spatial layout of these colors. However, two drawbacks of SMOG are still obvious, firstly, in the initialization of SMOG, some background pixels are inevitably introduced and clustered as an object mode for tracking, this often degenerates the tracking performance. Secondly, the weight of each Gaussian mode is restricted by the probability of the pixels belong to it, so a low probability Gaussian mode always contribute a little in similarity measure even it has a high discrimination for discriminating the object. A revised SMOG model is proposed to efficiently cope with these two problems by sufficiently considering the object local background. Experiment results on synthetic and real image sequences verified the validity of the revised model.

Citation Download Citation

Huan Wang, Mingwu Ren, and Jingyu Yang "Object tracking with revised SMOG model", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880Y (15 November 2007); https://doi.org/10.1117/12.748682

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