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An Adaptive Superpixel Tracker Using Multiple Features

Jingjing Liu1,2, Bin Zhang3, Xu Cheng4, Ying Chen5, Li Zhao1,*

School of Information Science and Engineering, Southeast University, Nanjing, 210096, China.
College of Information Science and Engineering, Henan University of Technology, ZhengZhou, 450001, China.
Enterprise and Information Technology, Henan Branch of China Telecom Co., Ltd, Zhengzhou, 450046, China.
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Department of Psychiatry, Columbia University in the City of New York and NYSPI, New York, NY 10032, USA.

* Corresponding Author: Li Zhao. Email: email.

Computers, Materials & Continua 2019, 60(3), 1097-1108.


Visual tracking is a challenging issue in the field of computer vision due to the objects’ intricate appearance variation. To adapt the change of the appearance, multiple channel features which could provide more information are used. However, the low level feature could not represent the structure of the object. In this paper, a superpixel-based adaptive tracking algorithm by using color histogram and haar-like feature is proposed, whose feature is classified into the middle level. Based on the superpixel representation of video frames, the haar-like feature is extracted at the superpixel level as the local feature, and the color histogram feature is applied with the combination of background subtraction method as the frame feature. Then, local features are clustered and weighted according to the target label and the location center. Superpixel-based appearance model is measured by using the sum of the voting map, and the candidate with the highest score is selected as the tracking result. Finally, an efficient template updating scheme is introduced to obtain the robust results and improve the computational efficiency. The proposed algorithm is evaluated on eight challenging video sequences and experimental results demonstrate that the proposed method can get better performance on occlusion, illumination variation and transformation.


Cite This Article

J. Liu, B. Zhang, X. Cheng, Y. Chen and L. Zhao, "An adaptive superpixel tracker using multiple features," Computers, Materials & Continua, vol. 60, no.3, pp. 1097–1108, 2019.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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