It is a difficult task to find an observation model that will perform well for long-term visual tracking. In this paper, we propose an adaptive observation enhancement technique based on likelihood images, which are derived from multiple visual features. The most discriminative likelihood image is extracted by principal component analysis (PCA) and incrementally updated frame by frame to reduce temporal tracking error. In the particle filter framework, the feasibility of each sample is computed using this most discriminative likelihood image before the observation process. Integral image is employed for efficient computation of the feasibility of each sample. We illustrate how our enhancement technique contributes to more robust observations through demonstrations.


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    Title :

    Robust observations for object tracking


    Contributors:
    Bohyung Han, (author) / Davis, L. (author)


    Publication date :

    2005-01-01


    Size :

    261715 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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