SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking

Authors

  • Qintao Hu University of Chinese Academy of Sciences
  • Lijun Zhou University of Chinese Academy of Sciences
  • Xiaoxiao Wang University of California, Davis
  • Yao Mao Institute of Optics and Electronics, Chinese Academy of Sciences
  • Jianlin Zhang Institute of Optics and Electronics, Chinese Academy of Sciences
  • Qixiang Ye University of Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v34i07.6733

Abstract

Modern visual trackers usually construct online learning models under the assumption that the feature response has a Gaussian distribution with target-centered peak response. Nevertheless, such an assumption is implausible when there is progressive interference from other targets and/or background noise, which produce sub-peaks on the tracking response map and cause model drift. In this paper, we propose a rectified online learning approach for sub-peak response suppression and peak response enforcement and target at handling progressive interference in a systematic way. Our approach, referred to as SPSTracker, applies simple-yet-efficient Peak Response Pooling (PRP) to aggregate and align discriminative features, as well as leveraging a Boundary Response Truncation (BRT) to reduce the variance of feature response. By fusing with multi-scale features, SPSTracker aggregates the response distribution of multiple sub-peaks to a single maximum peak, which enforces the discriminative capability of features for robust object tracking. Experiments on the OTB, NFS and VOT2018 benchmarks demonstrate that SPSTrack outperforms the state-of-the-art real-time trackers with significant margins1

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Published

2020-04-03

How to Cite

Hu, Q., Zhou, L., Wang, X., Mao, Y., Zhang, J., & Ye, Q. (2020). SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 10989-10996. https://doi.org/10.1609/aaai.v34i07.6733

Issue

Section

AAAI Technical Track: Vision