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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A Robust Visual Tracking Method Based on Reconstruction Patch Transformer Tracking.

Hui Chen1, Zhenhai Wang1, Hongyu Tian2

  • 1College of Information Science and Engineering, Linyi University, Linyi 276000, China.

Sensors (Basel, Switzerland)
|September 9, 2022
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Summary

This study introduces a novel reconstruction patch strategy for transformer-based visual target tracking. This method enhances feature correlation and speeds up tracking by optimizing transformer inputs.

Keywords:
CNNcross-attentiontransformertransformer-based tracker

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Transformer models are advancing in visual target tracking, replacing cross-correlation with cross-attention.
  • Current transformer trackers use Convolutional Neural Networks (CNNs) for feature extraction, deforming them into patches for transformer encoders.

Purpose of the Study:

  • To propose a new reconstruction patch strategy for transformer-based visual target tracking.
  • To improve the efficiency and speed of transformer-based trackers.

Main Methods:

  • A novel reconstruction patch strategy is introduced, combining CNN-extracted features with multiple spatial elements into new patches.
  • The performer operation is utilized to reduce computational load and patch dimensions for the transformer.

Main Results:

  • The reconstruction strategy effectively combines correlations between adjacent elements, enhancing CNN feature usability for classification and regression.
  • Reduced network computation and transformer input dimensions lead to fewer network parameters and faster tracking speeds.

Conclusions:

  • The proposed reconstruction patch strategy offers significant improvements in computational efficiency and tracking speed for transformer-based visual target tracking.
  • This approach enhances the integration of CNN features within transformer architectures for improved performance.