Abstract In this paper, we adaptively model the appearance of objects based on Mixture of Gaussians in a joint spatial-color space (the approach is called SMOG). We propose a new SMOG-based similarity measure. SMOG captures richer information than the general color histogram because it incorporates spatial layout in addition to color. This appearance model and the similarity measure are used in a framework of Bayesian probability for tracking natural objects. In the second part of the paper, we propose an Integral Gaussian Mixture (IGM) technique, as a fast way to extract the parameters of SMOG for target candidate. With IGM, the parameters of SMOG can be computed efficiently by using only simple arithmetic operations (addition, subtraction, division) and thus the computation is reduced to linear complexity. Experiments show that our method can successfully track objects despite changes in foreground appearance, clutter, occlusion, etc.; and that it outperforms several color-histogram based methods.


    Access

    Download


    Export, share and cite



    Title :

    Effective Appearance Model and Similarity Measure for Particle Filtering and Visual Tracking


    Contributors:


    Publication date :

    2006-01-01


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Effective Appearance Model and Similarity Measure for Particle Filtering and Visual Tracking

    Wang, H. / Suter, D. / Schindler, K. | British Library Conference Proceedings | 2006


    Appearance-guided particle filtering for articulated hand tracking

    Wen-Yan Chang, / Chu-Song Chen, / Yi-Ping Hung, | IEEE | 2005


    Adaptive on-line similarity measure for direct visual tracking

    Firouzi, H. / Najjaran, H. | British Library Online Contents | 2014


    Retrieving images by similarity of visual appearance

    Ravela, S. / Manmatha, R. | IEEE | 1997


    Retrieving Images by Similarity of Visual Appearance

    Ravela, S. / Manmatha, R. / IEEE; Computer Society; Technical Committee on PAMI | British Library Conference Proceedings | 1997