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A Meta-Q-Learning Approach to Discriminative Correlation Filter based Visual Tracking

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Abstract

Visual object tracking remains a challenging computer vision problem with numerous real-world applications. Discriminative correlation filter (DCF)-based methods are a recent state-of-the-art approach for dealing with this problem. The learning rate when applying a DCF is typically fixed, regardless of the situation. However, this rate is important for robust tracking, insofar as real-world video sequences include a variety of dynamical changes, such as occlusions, motion blur, and deformations. In this study, we propose Meta-Q-learning Correlation Filter (MQCF), a method for dynamically determining the learning rate of a baseline DCF-based tracker based on hand-crafted features of Histogram of Oriented Gradient (HOG), by means of reinforcement learning. The incorporation of reinforcement learning enables us to train a function for an image patch that outputs a situation-dependent learning rate of the baseline tracker in an autonomous fashion. We evaluated this method using two open benchmarks, namely, OTB-2015 and VOT-2105, and found our MQCF tracker outperformed a baseline state-of-the-art tracker by 1.8% in Area Under Curve on OTB-2015, and 8.4% relative gain in Expected Average Overlap in the VOT-2015 challenge. Our results demonstrate the advantages of the so-called meta-learning with DCF-based visual object tracking.

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Acknowledgments

The authors would like to thank the associate editor and anonymous reviewers for their time and constructive comments towards the improvement of our work. This research was partly supported by JSPS-KAKENHI (No. 17H06310 and 19H04180).

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Authors and Affiliations

  1. Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, Japan

    Akihiro Kubo & Shin Ishii

  2. The RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

    Kourosh Meshgi

Authors
  1. Akihiro Kubo
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  2. Kourosh Meshgi
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  3. Shin Ishii
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Correspondence to Akihiro Kubo.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Kubo, A., Meshgi, K. & Ishii, S. A Meta-Q-Learning Approach to Discriminative Correlation Filter based Visual Tracking. J Intell Robot Syst 101, 11 (2021). https://doi.org/10.1007/s10846-020-01273-2

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