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Geometry of Motion for Video Shakiness Detection

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Abstract

This paper presents a novel algorithm for automatically detecting global shakiness in casual videos. Perframe amplitude is computed by the geometry of motion, based on the kinematic model defined by inter-frame geometric transformations. Inspired by motion perception, we investigate the just-noticeable amplitude of shaky motion perceived by the human visual system. Then, we use the thresholding contrast strategy on the statistics of per-frame amplitudes to determine the occurrence of perceived shakiness. For testing the detection accuracy, a dataset of video clips is constructed with manual shakiness label as the ground truth. The experiments demonstrate that our algorithm can obtain good detection accuracy that is in concordance with subjective judgement on the videos in the dataset.

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Authors and Affiliations

  1. School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China

    Xiao-Qun Wu, Hai-Sheng Li, Jian Cao & Qiang Cai

  2. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China

    Xiao-Qun Wu, Hai-Sheng Li, Jian Cao & Qiang Cai

Authors
  1. Xiao-Qun Wu
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  2. Hai-Sheng Li
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  3. Jian Cao
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  4. Qiang Cai
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Correspondence to Xiao-Qun Wu.

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Cite this article

Wu, XQ., Li, HS., Cao, J. et al. Geometry of Motion for Video Shakiness Detection. J. Comput. Sci. Technol. 33, 475–486 (2018). https://doi.org/10.1007/s11390-018-1832-5

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  • DOI: https://doi.org/10.1007/s11390-018-1832-5

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