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

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Why You Cannot Rank First: Modifications for Benchmarking Six-Degree-of-Freedom Visual Localization Algorithms.

Sheng Han^1,2, Wei Gao^1,2, Zhanyi Hu^1,2

¹School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This summary is machine-generated.

This study enhances long-term visual localization benchmarks for autonomous vehicles by standardizing preprocessing and introducing new evaluation metrics. These improvements ensure more accurate and reliable performance assessments in challenging real-world conditions.

Keywords:

benchmark enhancement pose compensation sequential interpolation ties resolution visual localization

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Area of Science:

Computer Vision
Robotics
Spatial Perception

Background:

Visual localization faces challenges due to appearance variations from seasons and lighting.
Existing benchmarks for long-term visual localization need improvement, particularly for autonomous vehicles.
Advancements in technology have led to more datasets, promoting progress in 6-DOF visual localization.

Purpose of the Study:

To rectify limitations in current public benchmarks for long-term visual localization.
To enhance the evaluation algorithm's rationality and comprehensiveness for autonomous vehicle challenges.
To ensure fair and accurate algorithmic performance evaluation.

Main Methods:

Standardized preprocessing procedures including camera pose alignment and sequence information incorporation.
Replacing individual camera positions with uniform vehicle poses for consistent evaluation.
Introduction of a novel indicator to resolve ranking ties in the Schulze method.

Main Results:

Proposed modifications significantly improve the evaluation of long-term visual localization.
Simulations and experiments confirm the necessity and effectiveness of the amendments.
Standardized procedures reduce human intervention and enhance result reliability.

Conclusions:

The study provides a more robust and comprehensive benchmark for long-term visual localization.
The proposed amendments are essential for accurate assessment of autonomous vehicle localization systems.
The novel tie-breaking indicator ensures a clearer ranking of submitted methods.