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Updated: Dec 7, 2025

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

Dual-regression model for visual tracking.

Xin Li¹, Qiao Liu¹, Nana Fan¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.

Neural Networks : the Official Journal of the International Neural Network Society

|September 29, 2020

Summary

This summary is machine-generated.

This study introduces a dual-regression visual tracking framework that combines a convolutional module for discriminative ability and a correlation filter for accurate localization. The novel approach enhances tracking robustness and accuracy, outperforming existing methods.

Keywords:

Full convolutional network Object tracking Regression tracking model

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Last Updated: Dec 7, 2025

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Published on: October 18, 2018

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Existing regression-based visual tracking methods often compromise between accuracy and robustness.
Correlation filter and convolution models have limitations when addressing challenges like deformation and distractors simultaneously.

Purpose of the Study:

To propose a novel dual-regression framework for visual tracking that integrates both accuracy and robustness.
To develop a tracker capable of handling challenging scenarios including drastic deformation, distractors, and complex backgrounds.

Main Methods:

A discriminative fully convolutional module trained with hard negative mining for robust feature extraction.
A fine-grained correlation filter component utilizing shallow and fine-grained features for precise localization.
A coarse-to-fine fusion strategy combining the outputs of both modules.

Main Results:

The proposed dual-regression framework demonstrates superior tracking performance.
Achieved favorable results against state-of-the-art methods on OTB2013, OTB2015, and VOT2015 datasets.
Successfully handled challenging tracking scenarios like drastic deformation and complex backgrounds.

Conclusions:

The dual-regression tracking framework effectively balances accuracy and robustness.
The proposed method offers a significant advancement in visual tracking technology.
The approach provides a robust and accurate solution for real-world visual tracking applications.