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Ocean: Object-Aware Anchor-Free Tracking

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

Anchor-based Siamese trackers have achieved remarkable advancements in accuracy, yet the further improvement is restricted by the lagged tracking robustness. We find the underlying reason is that the regression network in anchor-based methods is only trained on the positive anchor boxes (i.e., \(IoU\ge 0.6\)). This mechanism makes it difficult to refine the anchors whose overlap with the target objects are small. In this paper, we propose a novel object-aware anchor-free network to address this issue. First, instead of refining the reference anchor boxes, we directly predict the position and scale of target objects in an anchor-free fashion. Since each pixel in groundtruth boxes is well trained, the tracker is capable of rectifying inexact predictions of target objects during inference. Second, we introduce a feature alignment module to learn an object-aware feature from predicted bounding boxes. The object-aware feature can further contribute to the classification of target objects and background. Moreover, we present a novel tracking framework based on the anchor-free model. The experiments show that our anchor-free tracker achieves state-of-the-art performance on five benchmarks, including VOT-2018, VOT-2019, OTB-100, GOT-10k and LaSOT. The source code is available at https://github.com/researchmm/TracKit.

Z. Zhang—Work performed when Zhipeng was an intern of Microsoft Research.

Z. Zhang, B. Li, W. Hu are with the Institution of Automation, Chinese Academy of Sciences (CASIA) and School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS) and CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT).

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Acknowledgement

This work is supported by the Natural Science Foundation of China (Grant No. U1636218, 61751212, 61721004, 61902401, 61972071), Natural Science Foundation of Beijing (Grant No.L172051), the CAS Key Research Program of Frontier Sciences (Grant No. QYZDJ-SSW-JSC040), and the Natural Science Foundation of Guangdong (No. 2018B030311046).

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Authors and Affiliations

  1. NLPR, CASIA and AI School, UCAS and CEBSIT, Beijing, China

    Zhipeng Zhang, Bing Li & Weiming Hu

  2. Microsoft Research, Beijing, China

    Zhipeng Zhang, Houwen Peng & Jianlong Fu

Authors
  1. Zhipeng Zhang
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  2. Houwen Peng
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  3. Jianlong Fu
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  4. Bing Li
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  5. Weiming Hu
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Correspondence to Houwen Peng .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Zhang, Z., Peng, H., Fu, J., Li, B., Hu, W. (2020). Ocean: Object-Aware Anchor-Free Tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12366. Springer, Cham. https://doi.org/10.1007/978-3-030-58589-1_46

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  • DOI: https://doi.org/10.1007/978-3-030-58589-1_46

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