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Robust visual tracking based on scale invariance and deep learning

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Abstract

Visual tracking is a popular research area in computer vision, which is very difficult to actualize because of challenges such as changes in scale and illumination, rotation, fast motion, and occlusion. Consequently, the focus in this research area is to make tracking algorithms adapt to these changes, so as to implement stable and accurate visual tracking. This paper proposes a visual tracking algorithm that integrates the scale invariance of SURF feature with deep learning to enhance the tracking robustness when the size of the object to be tracked changes significantly. Particle filter is used for motion estimation. The confidence of each particle is computed via a deep neural network, and the result of particle filter is verified and corrected by mean shift because of its computational efficiency and insensitivity to external interference. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods throughout the challenging factors in visual tracking, especially for scale variation.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61320106006, 61532006, 61502042).

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Authors and Affiliations

  1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Nan Ren, Junping Du, Suguo Zhu, Linghui Li & Dan Fan

  2. Department of Electronics Engineering, Pusan National University, Busan, 46241, Korea

    JangMyung Lee

Authors
  1. Nan Ren
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  2. Junping Du
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  3. Suguo Zhu
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  4. Linghui Li
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  5. Dan Fan
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  6. JangMyung Lee
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Corresponding author

Correspondence to Junping Du.

Additional information

Nan Ren is a graduate student in School of Computer Science, Beijing University of Posts and Telecommunications, China. She received her bachelor’s degree in computer science and technology from University of Science and Technology Beijing, China in 2014. Her research interest is mainly image processing and visual tracking.

Junping Du is now a full professor and PhD supervisor in School of Computer Science and Technology, Beijing University of Posts and Telecommunications, China. Her research interests include artificial intelligence, image processing and pattern recognition.

Suguo Zhu received her MS degree in computer science and technology from Guangdong University of Technology, China in 2012. She is now a PhD candidate in computer science of Beijing University of Posts and Telecommunications, China. Her research interests include image processing, computer vision and visual tracking.

Linghui Li received her BS degree in computer science and technology from Xi’an University of Posts and Telecommunications, China. She is now studying for a master’s degree in computer science and technology from Beijing University of Posts and Telecommunications, China. Her research interests include image processing and computer vision.

Dan Fan received the BS degree in computer science and technology from Shandong University, China. He is currently pursuing the master’s degree. His current research interests include image processing and computer vision.

JangMyung Lee received the BS and MS degrees in electronic engineering from Seoul National University, Korea in 1980 and 1982, respectively, and the PhD degree in computer engineering from the University of Southern California, Los Angeles, USA in 1990. Since 1992, he has been a professor with Pusan National University, Korea. He is a research group leader for Logistics and IT. His current research interests include intelligent robotic systems, ubiquitous port, and intelligent sensors. Prof. Lee is a former chairman of the Research Institute of Computer, Information, and Communication (RICIC). He is currently serving as the director of the Institute of Control Automation, and Systems Engineers (ICASE), Institute of Electronics Engineers, Korea (IEEK), Korea Robotics Society (KROS), and Society of Instrument and Control Engineers (SICE).

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Ren, N., Du, J., Zhu, S. et al. Robust visual tracking based on scale invariance and deep learning. Front. Comput. Sci. 11, 230–242 (2017). https://doi.org/10.1007/s11704-016-6050-0

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  • DOI: https://doi.org/10.1007/s11704-016-6050-0

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