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2D scale-adaptive tracking based on projective geometry
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DOI：10.1007/s11042-013-1407-y.png)
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摘要
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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.
Keyword:
Mean-shift
Projective geometry
Tracking
Scale adaptation
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3
论文数:
2.0W
被引数:
3.2W
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