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Robust feature learning for online discriminative tracking without large-scale pre-training

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Abstract

Owing to the inherent lack of training data in visual tracking, recent work in deep learning-based trackers has focused on learning a generic representation offline from large-scale training data and transferring the pre-trained feature representation to a tracking task. Offline pre-training is time-consuming, and the learned generic representation may be either less discriminative for tracking specific objects or overfitted to typical tracking datasets. In this paper, we propose an online discriminative tracking method based on robust feature learning without large-scale pre-training. Specifically, we first design a PCA filter bank-based convolutional neural network (CNN) architecture to learn robust features online with a few positive and negative samples in the high-dimensional feature space. Then, we use a simple soft-thresholding method to produce sparse features that are more robust to target appearance variations. Moreover, we increase the reliability of our tracker using edge information generated from edge box proposals during the process of visual tracking. Finally, effective visual tracking results are achieved by systematically combining the tracking information and edge box-based scores in a particle filtering framework. Extensive results on the widely used online tracking benchmark (OTB-50) with 50 videos validate the robustness and effectiveness of the proposed tracker without large-scale pre-training.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61572205 and 61175121), Natural Science Foundation of Fujian Province (2015J01257), Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (ZQN-PY210 and ZQN-YX108), 2015 Program for New Century Excellent Talents in Fujian Province University, Project of science and technology plan of Fujian Province of China (2017H01010065).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Huaqiao University, Quanzhou, 361021, China

    Jun Zhang, Bineng Zhong, Pengfei Wang, Cheng Wang & Jixiang Du

Authors
  1. Jun Zhang
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  2. Bineng Zhong
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  3. Pengfei Wang
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  4. Cheng Wang
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  5. Jixiang Du
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Corresponding author

Correspondence to Bineng Zhong.

Additional information

Jun Zhang received the BS degree from the Hunan Institute of Science and Technology, Hunan, China in 2014. She is currently pursuing the ME degree with the school of Huaqiao University, Fujian, China. Her current research interests include computer vision, machine learning, and pattern recognition.

Bineng Zhong received the BS, MS, and PhD degrees in computer science from the Harbin Institute of Technology, Harbin, China in 2004, 2006, and 2010, respectively. From 2007 to 2008, he was a research fellow with the Institute of Automation and Institute of Computing Technology, Chinese Academy of Science, China. Currently, he is an associate professor with the School of Computer Science and Technology, Huaqiao University, Xiamen, China. His current research interests include pattern recognition, machine learning, and computer vision.

Pengfei Wang received the BS degree in College of Mathematic and Information, China West Normal University, Nanchong, China in 2014. Currently, he is a master student with the School of Computer Science and Technology, Huaqiao University, Xiamen, China. His current research interests include object tracking, machine learning, and computer vision.

Cheng Wang received the BS degree in software engineering from Xi’dian University, Xi’an, China in 2002 and the PhD degree in mechanics from Xi’an Jiaotong University, Xi’an, China in 2012, respectively. Currently, he is an associate professor at the School of Computer Science and Technology, Huaqiao University, Xiamen, China. His current research interests include signal processing, machine learning, and data mining.

Jixiang Du received the BS andMS degrees in Vehicle Engineering from Hefei University of Technology, Hefei, China in 1999 and 2002. He received the PhD degree in Pattern Recognition & Intelligent System from University of Science and Technology of China (USTC), Hefei, China in 2005. He is also the Associate Dean of the College and the Director of Department of Computer Science and Technology. His current research mainly concern pattern recognition and machine learning.

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Zhang, J., Zhong, B., Wang, P. et al. Robust feature learning for online discriminative tracking without large-scale pre-training. Front. Comput. Sci. 12, 1160–1172 (2018). https://doi.org/10.1007/s11704-017-6281-8

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