Abstract
Active contours based tracking methods have widely used for object tracking due to their following advantages. 1) effectiveness to descript complex object boundary, and 2) ability to track the dynamic object boundary. However their tracking results are very sensitive to location of the initial curve. Initial curve far form the object induces more heavy computational cost, low accuracy of results, as well as missing the highly active object. Therefore, this paper presents an object tracking method using a mean shift algorithm and active contours. The proposed method consists of two steps: object localization and object extraction. In the first step, the object location is estimated using mean shift. And the second step, at the location, evolves the initial curve using an active contour model. To assess the effectiveness of the proposed method, it is applied to synthetic sequences and real image sequences which include moving objects.
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© 2005 Springer-Verlag Berlin Heidelberg
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Chang, J.S., Kim, E.Y., Jung, K., Kim, H.J. (2005). Object Tracking Using Mean Shift and Active Contours. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_4
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DOI: https://doi.org/10.1007/11504894_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
Online ISBN: 978-3-540-31893-4
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