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Incremental online object tracking via superpixel dimension reduction

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Abstract

Visual object tracking based on the principal component analysis (PCA) has been widely used in the last few decades as it deals with appearance changes caused by in-plane rotation, out-of-plane rotation, illumination variation and motion blur. But this method fails to handle partial occlusion problem. Also, it has computational complexity and speed problem when the size of image resolution is large. In this paper, a new method is proposed based on superpixel, PCA and sparse prototypes. The size of the image is reduced with preserving much of the visual information by superpixel segmentation. Then, the incremental PCA subspace representation is applied to model target appearance and account of occlusion with trivial templates. Both qualitative and quantitative analyses are done in many challenging image sequences. The proposed method gives a better performance than that of the other state-of-the-art algorithms.

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Authors and Affiliations

  1. Department of Computer Science, NIT Manipur, Imphal, India

    Rajkumari Bidyalakshmi Devi, Yambem Jina Chanu & Khumanthem Manglem Singh

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  1. Rajkumari Bidyalakshmi Devi
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  2. Yambem Jina Chanu
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  3. Khumanthem Manglem Singh
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Correspondence to Rajkumari Bidyalakshmi Devi.

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Devi, R.B., Chanu, Y.J. & Singh, K.M. Incremental online object tracking via superpixel dimension reduction. SIViP 14, 187–194 (2020). https://doi.org/10.1007/s11760-019-01541-1

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  • DOI: https://doi.org/10.1007/s11760-019-01541-1

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