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JFT: A Robust Visual Tracker Based on Jitter Factor and Global Registration

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13537))

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Abstract

Visual object tracking is an important yet challenging task in computer vision, whose accuracy is highly subject to the problems of camera motion/shake and occlusion. In order to solve these two challenging problems and improve the tracking accuracy of real scenes, this paper proposed a robust visual object tracker based on Jitter Factor and global registration. The proposed tracker firstly extracts the histogram of oriented gradient (HOG) features and color features of the target object to train the correlation filter. When the response map is unreliable, the proposed tracker treats the tracking problems as a global background motion and target motion problem, and then evaluates the state (tracking or missing) of the target by using Jitter Factor. If the target is assumed to be missing, global image registration and correlated Kalman filter would be applied to correct and predict the corrected target position. Experimental results on RGB-T234 show that after introducing the proposed Jitter Factor and global image registration, the correlation-filter-based trackers gained a ≥ 2.2% increase in precision rate.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2019YFC1511102) and the National Natural Science Foundation of China (No. 12002215).

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Authors and Affiliations

  1. College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China

    Haopeng Wang, Shuixing Pan, Dilong Li, Yueqiang Zhang, Linyu Huang, Hongxi Guo, Xiaolin Liu & Qifeng Yu

  2. Institute of Intelligent Optical Measurement and Detection, Shenzhen University, Shenzhen, 518060, China

    Haopeng Wang, Shuixing Pan, Dilong Li, Yueqiang Zhang, Linyu Huang, Hongxi Guo, Xiaolin Liu & Qifeng Yu

Authors
  1. Haopeng Wang
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  2. Shuixing Pan
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  3. Dilong Li
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  4. Yueqiang Zhang
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  5. Linyu Huang
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  6. Hongxi Guo
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  7. Xiaolin Liu
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  8. Qifeng Yu
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Corresponding author

Correspondence to Yueqiang Zhang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

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

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

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

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Wang, H. et al. (2022). JFT: A Robust Visual Tracker Based on Jitter Factor and Global Registration. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_54

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  • DOI: https://doi.org/10.1007/978-3-031-18916-6_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18915-9

  • Online ISBN: 978-3-031-18916-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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