Browsing by Author "Juan, Chung-Wei"

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3D object tracking using mean-shift and similarity-based aspect-graph modeling
(2007)
Hu, Jwu-Sheng
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Su, Tzung-Min
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Juan, Chung-Wei
;
Wang, George
;
電控工程研究所
The mean shift algorithm is a popular method in the field of 2D object tracking due to its simplicity and robustness over slight variations of lighting condition, scale and view-point over time. However, the appearance of 3D object might have distinctive variations for different viewpoints over time. In this work, a novel method for tracking 3D objects using mean-shift algorithm and a 3D object database is proposed to achieve a more precise tracking. A 3D obctject database using similarity-based aspect-graph is built from 2D images sampled at random intervals from the viewing sphere. Contour and color features of each 2D image are used for modeling the 3D object database. To conduct tracking, a suitable object model is selected from the database and the mean-shift tracking is applied to find the local minima of a similarity measure between the color histourams of the object model and the target image. The effectiveness of the proposed method is demonstrated by experiments with objects rotating and translating in space.
A new spatial-color mean-shift object tracking algorithm with scale and orientation estimation
(2008)
Juan, Chung-Wei
;
Hu, Jwu-Sheng
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, we propose a new mean-shift tracking algorithm based on a novel similarity measure function. The joint spatial-color feature is used as our basic model elements. The target image is modeled with the kernel density estimation and the new similarity measure functions is developed using the expectation of the estimated kernel density. With these new similarity measure functions, two similarity-based mean-shift tracking algorithms are derived. To enhance the robustness, the weighted background information is added into the proposed tracking algorithm. In order to solve the object deformation problem, the principal component analysis is used to update the orientation of the tracking object, and corresponding eigenvalues are used to monitor the scale of the object. The experimental results show that the new similarity-based tracking algorithms can be implemented in real-time and are able to track the moving object with an automatic update of the orientation and scale.
A spatial-color mean-shift object tracking algorithm with scale and orientation estimation
(2008-12-01)
Hu, Jwu-Sheng
;
Juan, Chung-Wei
;
Wang, Jyun-Ji
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, an enhanced mean-shift tracking algorithm using joint spatial-color feature and a novel similarity measure function is proposed. The target image is modeled with the kernel density estimation and new similarity measure functions are developed using the expectation of the estimated kernel density. With these new similarity measure functions, two similarity-based mean-shift tracking algorithms are derived. To enhance the robustness, the weighted-background information is added into the proposed tracking algorithm. Further, to cope with the object deformation problem, the principal components of the variance matrix are computed to update the orientation of the tracking object, and corresponding eigenvalues are used to monitor the scale of the object. The experimental results show that the new similarity-based tracking algorithms can be implemented in real-time and are able to track the moving object with an automatic update of the orientation and scale changes. (C) 2008 Elsevier B.V. All rights reserved.