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SiamDCFF: Dynamic Cascade Feature Fusion for Vision Tracking.

Jinbo Lu1, Na Wu1, Shuo Hu1

  • 1School of Electrical Engineering, Yanshan University, Qinhuangdao 066000, China.

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|July 27, 2024
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Summary

This study introduces a dynamic cascade feature fusion (DCFF) module to improve single-object tracking. The new module enhances global dependency modeling, significantly boosting tracker performance in Siamese network-based systems.

Keywords:
Siamese networkdynamic attentiondynamic cascade feature fusionfeature guidanceobject tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Accurate feature fusion is crucial for enhancing single-object tracking performance in Siamese network-based trackers.
  • Existing depth-wise cross-correlation modules struggle to establish global dependencies within feature maps of the search area.

Purpose of the Study:

  • To propose and validate a dynamic cascade feature fusion (DCFF) module for improving global dependency modeling in Siamese trackers.
  • To investigate the impact of global dependencies on the performance of fully convolutional Siamese network trackers.

Main Methods:

  • Introduced a dynamic cascade feature fusion (DCFF) module incorporating a local feature guidance (LFG) module and dynamic attention modules (DAMs).
  • Integrated the DCFF module into a Siamese network tracking framework, resulting in the proposed SiamDCFF model.
  • Conducted verification experiments to assess the effectiveness of global dependency establishment and evaluated SiamDCFF on public datasets.

Main Results:

  • Establishing global dependencies for features derived from depth-wise cross-correlation significantly improves fully convolutional Siamese network tracker performance.
  • The proposed SiamDCFF model demonstrated substantial performance improvements compared to the baseline model.
  • The DCFF module effectively enhances global dependency modeling capabilities during the feature fusion process.

Conclusions:

  • The dynamic cascade feature fusion (DCFF) module offers a viable solution for enhancing global dependency modeling in Siamese network trackers.
  • SiamDCFF represents a significant advancement in single-object tracking, providing more robust and accurate tracking capabilities.
  • The findings provide experimental support for the rational design of feature fusion modules in advanced tracking systems.