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Chinese Conference on Pattern Recognition

CCPR 2016: Pattern Recognition pp 426–436Cite as

Real-Time Object Tracking Using Dynamic Measurement Matrix

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 662))

Abstract

Object tracking has attracted a lot of attention over the past decades. Features represent the main and primary information of object, however, fixed and invariable feature extraction methods would make the features losing their representation. In this paper, we propose a novel robust single object tracking approach using Dynamic Measurement Matrix to extract dynamic features. In particular, we employ the dynamic measurement matrix to adaptively extract features for discriminating object and background so that features have better and clear representativeness. In additional, our approach is a tracking-by-detection approach via a Naive Bayes Classifier with online updating. Compared to traditional approaches, we not only utilize a Naive Bayes Classifier to classify samples but exploit the nature of this classifier to weigh each compressive feature unit, which would be used to update the measurement matrix. The proposed approach runs in real-time and is robust to pose variation, illumination change and occlusion. Furthermore, both quantitative and qualitative experiments results show that our approach has more stable and superior performance.

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Acknowledgments

The authors would like to thank all the reviewers for their insightful comments. This work was supported by the National Natural Science Foundation of China (Grant No.61305033, 61273256 and 6157021026), Fundamental Research Funds for the Central Universities (ZYGX2014Z009).

Author information

Authors and Affiliations

  1. Center for Robotics, University of Electronic Science and Technology of China, Sichuan, 611731, China

    Jing Li, Hong Cheng, Runzhou Wang & Lu Yang

Authors
  1. Jing Li
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  2. Hong Cheng
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  3. Runzhou Wang
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  4. Lu Yang
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Corresponding author

Correspondence to Hong Cheng .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Li, J., Cheng, H., Wang, R., Yang, L. (2016). Real-Time Object Tracking Using Dynamic Measurement Matrix. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_36

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  • DOI: https://doi.org/10.1007/978-981-10-3002-4_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3001-7

  • Online ISBN: 978-981-10-3002-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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