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Compared to the existing correlation filter-based methods, our tracker is insusceptible to the non-uniform distributions of the feature map, and can better suppress the background regions

Correlation Tracking Via Joint Discrimination And Reliability Learning

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), (2018): 489-497

Cited by: 139|Views60
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Abstract

For visual tracking, an ideal filter learned by the correlation filter (CF) method should take both discrimination and reliability information. However, existing attempts usually focus on the former one while pay less attention to reliability learning. This may make the learned filter be dominated by the unexpected salient regions on the ...More

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Introduction
  • Visual tracking is a hot topic for its wide applications in many computer vision tasks, such as video surveillance, behaviour analysis, augmented reality, to name a few.
  • By multiplying the filter with a binary mask, the tracker is able to generate the real training samples having the same size as the target object, and better suppressing the background regions
  • This method has two limitations: first, it exploits the augmented Lagrangian method for model learning, which limits the model extension; second, even though the background region outside the bounding box is suppressed, the tracker may be influenced by the background region inside the bounding box
Highlights
  • Visual tracking is a hot topic for its wide applications in many computer vision tasks, such as video surveillance, behaviour analysis, augmented reality, to name a few
  • As the correlation filter takes the entire image as the positive sample and the cyclically shifted images as negative ones, the learned filters are likely to be influenced by the background regions
  • We introduce a local response consistency constraint for the base filter, which constrains that each sub-region of the target has similar importance
  • The reliability information is separated from the base filter
  • We consider the reliability information in the filter, which is jointly learned with the base filter
  • Compared to the existing correlation filter-based methods, our tracker is insusceptible to the non-uniform distributions of the feature map, and can better suppress the background regions
Methods
  • Let y=[y1, y2, ..., yK ]⊤ ∈ RK×1 denote gaussian shaped response, and xd ∈ RK×1 be the input vector for the d-th channel, the correlation filter learns the optimal w by optimizing the following formula: K.
  • K=1 d=1 where xk,d is the k-step circular shift of the input vector xd, yk is the k-th element of y, w= w1⊤, w2⊤, ..., wD⊤ ⊤ where wd ∈ RK×1 stands for the filter of the d-th channel.
Results
  • The authors demonstrate the effectiveness of the proposed tracker on the OTB-2013 [30], OTB-2015 [31] and VOT-2016 [15] benchmark datasets.
  • Since the method jointly considers both discrimination and reliability for tracking, the authors denote it as DRT for clarity.
  • The proposed DRT method is mainly implemented in MATLAB and is partially accelerated with the Caffe toolkit [13].
  • The authors use a relatively small learning rate ω (i.e. 0.011) for first 10 frames to avoid model degradation with limited training samples, and use a larger one (i.e. 0.02) in the following tracking process.
  • One implementation of the tracker can be found in https://github.com/cswaynecool/DRT
Conclusion
  • The authors clearly consider the discrimination and reliability information in the correlation filter (CF) formula and rewrite the filter weight as the element-wise product of a base filter and a reliability weight map.
  • The authors introduce a local response consistency constraint for the base filter, which constrains that each sub-region of the target has similar importance.
  • By this means, the reliability information is separated from the base filter.
  • Compared to the existing CF-based methods, the tracker is insusceptible to the non-uniform distributions of the feature map, and can better suppress the background regions.
  • Extensive experiments demonstrate that the proposed tracker outperforms the state-of-the-art algorithms over all three benchmarks
Tables
  • Table1: Performance evaluation of different state-of-the-art trackers in the VOT-2016 dataset. In this dataset, we compare our DRT method with the top 10 trackers. The best two results are marked in red and blue bold fonts, respectively
Download tables as Excel
Funding
  • This paper is partially supported by the Natural Science Foundation of China #61502070, #61725202, #61472060
  • Chong Sun and Ming-Hsuan Yang are also supported in part by NSF CAREER (No 1149783), gifts from Adobe, Toyota, Panasonic, Samsung, NEC, Verisk, and Nvidia
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