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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Robust Visual Tracking Based on Adaptive Multi-Feature Fusion Using the Tracking Reliability Criterion.

Lin Zhou¹, Han Wang¹, Yong Jin¹

¹School of Computer and Information Engineering, Henan University, Kaifeng 475004, China.

Sensors (Basel, Switzerland)

|December 17, 2020

Summary

This summary is machine-generated.

This study introduces a new feature tracking reliability criterion and adaptive fusion framework to improve object tracking accuracy. A novel re-detection module enhances robustness against interference, reducing tracking failures.

Keywords:

adaptive feature fusion correlation filter feature tracking reliability criterion multiple online detectors visual object tracking

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Area of Science:

Computer Vision
Artificial Intelligence

Background:

Multi-resolution feature fusion using Discriminative Correlation Filter (DCF) methods has improved object tracking.
Existing methods suffer from tracking failures due to improper feature selection and fusion, and ineffective re-detection modules.

Purpose of the Study:

To propose a feature tracking reliability criterion to evaluate sample feature robustness and distinguishing ability.
To develop a novel feature adaptive fusion framework and a re-detection module to enhance object tracking performance and accuracy.

Main Methods:

Proposed a feature tracking reliability criterion to assess sample feature quality.
Developed a feature adaptive fusion framework integrating reliable features.
Introduced a re-detection module with multiple Support Vector Machine (SVM) detectors trained on diverse sample features for target recovery.

Main Results:

The proposed feature tracking reliability criterion effectively evaluates sample feature robustness.
The adaptive fusion framework and re-detection module significantly reduce tracking failures.
Experiments on OTB2015 and UAV123 datasets demonstrate superior accuracy and robustness compared to existing methods.

Conclusions:

The proposed feature tracking reliability criterion and adaptive fusion framework enhance object tracking performance.
The novel re-detection module effectively recovers from tracking failures and improves re-detection accuracy.
The method shows significant improvements in accuracy and robustness for object tracking applications.