Object tracking with the multi-templates regression model based MS algorithm
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초록
To deal with the problems of occlusion, pose variations and illumination changes in the object trackingsystem, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MSalgorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regressionmodel is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally,the geometric center of the tracked areas is considered as the object’s position. The proposed algorithm isevaluated on several classical videos. The experimental results show that the regression model weighted multitemplatesMS algorithm can track an object accurately in terms of occlusion, illumination changes and posevariations.
Abstract
To deal with the problems of occlusion, pose variations and illumination changes in the object trackingsystem, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MSalgorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regressionmodel is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally,the geometric center of the tracked areas is considered as the object’s position. The proposed algorithm isevaluated on several classical videos. The experimental results show that the regression model weighted multitemplatesMS algorithm can track an object accurately in terms of occlusion, illumination changes and posevariations.
참고문헌 (25)
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