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Highly non-rigid video object tracking using segment-based object candidates

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Abstract

A novel scheme for non-rigid video object tracking using segment-based object candidates is proposed in this paper. Rather than using a conventional bounding box, the tracker is based on segments and considers the target object to be a combination of segments, where the hierarchical hue-saturation-value histogram is extracted as a feature. The objectness method is employed and integrated into the tracker to generate candidates for a similarity measure. Moreover, segment-based motion weights are introduced to give higher weights to candidates with motion consistency. A confidence-collecting scheme is proposed for similar candidates. To validate our method, experiments were conducted using several image sequences with different non-rigid challenges. The experimental results show that the proposed scheme can achieve better performance than other state-of-the-art methods.

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Acknowledgments

This research was supported in part by the Research Committee of the University of Macau (MYRG2015-00011-FST, MYRG2015-00012-FST) and the Science and Technology Development Fund of Macau SAR (008/2013/A1, 093-2014-A2).

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Macau, SAR, China

    Cong Lin, Chi-Man Pun & Guoheng Huang

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  1. Cong Lin
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  2. Chi-Man Pun
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  3. Guoheng Huang
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Correspondence to Chi-Man Pun.

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Lin, C., Pun, CM. & Huang, G. Highly non-rigid video object tracking using segment-based object candidates. Multimed Tools Appl 76, 9565–9586 (2017). https://doi.org/10.1007/s11042-016-3563-3

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  • DOI: https://doi.org/10.1007/s11042-016-3563-3

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