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


Hua Zhang, Lijia Wang, Journal of Information Processing Systems Vol. 14, No. 6, pp. 1307-1317, Dec. 2018  

https://doi.org/10.3745/JIPS.02.0097
Keywords: Mean Shift Algorithm, Multi-Templates, object tracking, Regression Model
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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.


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Cite this article
[APA Style]
Zhang, H. & Wang, L. (2018). Object tracking with the multi-templates regression model based MS algorithm. Journal of Information Processing Systems, 14(6), 1307-1317. DOI: 10.3745/JIPS.02.0097.

[IEEE Style]
H. Zhang and 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.