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Object Tracking Using Mean Shift and Active Contours
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Computer Vision
Object Tracking Using Mean Shift and Active Contours
Jae Sik Chang1 , Eun Yi Kim2 , KeeChul Jung3 and Hang Joon Kim1 
| (1) |
Dept. of Computer Engineering, Kyungpook National Univ., South Korea |
| (2) |
Scool of Internet and Multimedia, NITRI (Next-Generation Innovative Technology Research Institute), Konkuk Univ., South Korea |
| (3) |
School of Media, College of Information Science, Soongsil University, |
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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