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  • Object tracking via non-Euclidean geometry: A Grassmann approach

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    Sanderson437163-Accepted.pdf (2.292Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Shirazi, Sareh
    Harandi, Mehrtash T
    Lovell, Brian C
    Sanderson, Conrad
    Griffith University Author(s)
    Sanderson, Conrad
    Year published
    2014
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    Abstract
    A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the above-mentioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the ...
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    A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the above-mentioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.
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    Conference Title
    IEEE Winter Conference on Applications of Computer Vision
    DOI
    https://doi.org/10.1109/wacv.2014.6836008
    Copyright Statement
    © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Numerical and computational mathematics
    Computer vision
    Theory of computation
    Machine learning not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/395926
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    • Conference outputs

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