Correlation Filter Tracking via Distractor-aware Learning and Multi-Anchor Detection

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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  • Guochun Chen
  • Gengzheng Pan
  • Yongxin Zhou
  • Wenxiong Kang
  • Feiqi Deng

Related Research Unit(s)


Original languageEnglish
Journal / PublicationIEEE Transactions on Circuits and Systems for Video Technology
Publication statusPublished - 24 Dec 2019


Correlation filter has demonstrated the power in object tracking, benefiting from its superior speed and competitive performance. However, existing correlation filter based trackers (CFTs) are fragile for some inherent defects caused by the boundary effect. To address this issue, we propose a novel correlation filter based tracking framework by integrating three highly collaborative components, including a fast target proposal module, a distractor-aware filter, and a correlation filter based refiner. Specifically, the target proposal aims at determining some target-like regions in contexts efficiently, which provides target-like patches to learn a distractor-aware filter and detect. Multi-region strategy enlarges space fields for learning and prediction. The filter learned from both target and distractors enhances its ability to identify background. Therefore, our method is capable of evaluating multiple candidates in wider context with less risk of drifting to distractors, namely multi-anchor detection. Besides, the proposed Proposal-Detect-Refine hierarchical searching process progressively achieves data alignment between testing and training samples, which benefits for reliable model prediction. A refiner is used to fine-tune positions after multi-anchor detection for lessening error accumulation and preventing model from drifting. Comprehensive experiments on five challenging datasets, i.e. OTB2013, OTB2015, VOT2017, VOT19, and TC128, demonstrate that the proposed method achieves superior performance against the state-of-the-art methods.

Research Area(s)

  • Correlation filter tracking, online learning, target proposal