Object tracking with the multi-templates regression model based MS algorithm

Hua Zhang and Lijia Wang
Volume: 14, No: 6, Page: 1307 ~ 1317, Year: 2018
10.3745/JIPS.02.0097
Keywords: Mean Shift Algorithm, Multi-Templates, Object tracking, Regression Model
Full Text:

Abstract
To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, 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 MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model 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 is evaluated on several classical videos. The experimental results show that the regression model weighted multitemplates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.

Article Statistics
Multiple requests among the same broswer session are counted as one view (or download).
If you mouse over a chart, a box will show the data point's value.


Cite this article
IEEE Style
H. Z. L. Wang, "Object tracking with the multi-templates regression model based MS algorithm," Journal of Information Processing Systems, vol. 14, no. 6, pp. 1307~1317, 2018. DOI: 10.3745/JIPS.02.0097.

ACM Style
Hua Zhang and Lijia Wang. 2018. Object tracking with the multi-templates regression model based MS algorithm, Journal of Information Processing Systems, 14, 6, (2018), 1307~1317. DOI: 10.3745/JIPS.02.0097.