Abstract
The mean shift algorithm is an efficient technique for object tracking. However, it has a shortcoming that it can’t adjust scale with object during tracking process. There are presently no effective ways to solve this problem. The kernel bandwidth of mean shift tracker in one frame is generally steered by the object scale obtained in the previous frame, so it is very important for mean shift tracker to correctly describe the scale of the target in very frame. In accordance with the kernel-bandwidth effect on the mean shift tracker and the property of face, this paper introduces a new idea that uses direct least square ellipse fitting to adjust the facial scale. The experimental results demonstrate the efficiency of this algorithm. Its performance has been proven superior to the original mean shift tracking algorithm.
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© 2006 Springer-Verlag Berlin Heidelberg
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Gao, J., Wu, Z., Wang, Y. (2006). Face Tracking Algorithm Based on Mean Shift and Ellipse Fitting. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_30
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DOI: https://doi.org/10.1007/11893257_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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