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Robust Visual Tracking Based on Convex Hull with EMD-L1

  • Representation, Processing, Analysis, and Understanding of Images
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Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

Factors such as drastic illumination variations, partial occlusion, rotation make robust visual tracking a difficult problem. Some tracking algorithms represent a target appearances based on obtained tracking results from previous frames with a linear combination of target templates. This kind of target representation is not robust to drastic appearance variations. In this paper, we propose a simple and effective tracking algorithm with a novel appearance model. A target candidate is represented by convex combinations of target templates. Measuring the similarity between a target candidate and the target templates is a key problem for a robust likelihood evaluation. The distance between a target candidate and the templates is measured using the earth mover’s distance with L1 ground distance. Comprehensive experiments demonstrate the robustness and effectiveness of the proposed tracking algorithm against state-of-the-art tracking algorithms.

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Author information

Authors and Affiliations

  1. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, 330099, China

    Jun Wang, Yuanyun Wang, Chengzhi Deng & Shengqian Wang

  2. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Jun Wang, Yuanyun Wang, Chengzhi Deng & Shengqian Wang

Authors
  1. Jun Wang
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  2. Yuanyun Wang
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  3. Chengzhi Deng
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  4. Shengqian Wang
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Corresponding author

Correspondence to Chengzhi Deng.

Additional information

The article is published in the original.

Jun Wang is an Associate Professor in the School of Information Engineering at Nanchang Institute of Technology, China. He received the M.S. degree in Computer Science and Technology from Nanchang University, China, in 2007 and the PhD in Artificial Intelligence from Xiamen University, China, in 2015, respectively. His research interests include visual tracking and pattern recognition.

Yuanyun Wang is an Assistant Professor in the School of Information Engineering at Nanchang Institute of Technology, China. She received the B.S. degree and M.S degree in Computer Science and Technology from Nanchang University, China, in 2004 and 2007 respectively. Her research interest is in visual tracking and pattern recognition.

Chengzhi Deng is a Professor in the School of Information Engineering at Nan-chang Institute of Technology, China. He received the M.S. degree in Communication Engineering from Jiangxi Normal University, China, in 2005 and the PhD in Information and Communication Engineering from Huazhong University of Science and Technology, China, in 2008, respectively. His research interest is in pattern recognition and image reconstruction.

Shengqian Wang is a Professor in the School of Information Engineering at Nan-chang Institute of Technology, China. He received the M.S. degree in Theoretical Physics from Jiangxi Normal University, China, in 1995 and the PhD in Communication and Information System from Shanghai Jiao Tong University, China, in 2002. His research interest is in super-resolution and pattern recognition.

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Wang, J., Wang, Y., Deng, C. et al. Robust Visual Tracking Based on Convex Hull with EMD-L1. Pattern Recognit. Image Anal. 28, 44–52 (2018). https://doi.org/10.1134/S1054661818010078

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  • DOI: https://doi.org/10.1134/S1054661818010078

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