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2D scale-adaptive tracking based on projective geometry

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Abstract

Object tracking is a fundamental challenge in computer vision. For real-time tracking, the efficiency and robustness of the Mean-shift algorithm makes it a popular choice. However, the scale of the Mean-shift kernel is a crucial parameter and no clear mechanism exists presently for updating the scale when a size-changing object is tracked. In this paper, a new method is introduced using projective geometry to determine the size of the object, and in turn the scale of the Mean-shift kernel. In the initial step of the algorithm, the geometric information of the scene is obtained automatically (or manually). With the geometric information, the size of the object is updated. The experimental results show that this algorithm is stable, efficient and outperforms the Mean-shift baseline and other kernel updating methods, such as CAMSHIFT.

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Authors and Affiliations

  1. State Key Lab. of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xian, Shaanxi, China, 710071

    Zhongyu Lou, Guang Jiang & Chengke Wu

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  1. Zhongyu Lou
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  2. Guang Jiang
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  3. Chengke Wu
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Correspondence to Zhongyu Lou.

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Lou, Z., Jiang, G. & Wu, C. 2D scale-adaptive tracking based on projective geometry. Multimed Tools Appl 72, 905–924 (2014). https://doi.org/10.1007/s11042-013-1407-y

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  • DOI: https://doi.org/10.1007/s11042-013-1407-y

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