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Related Experiment Video

Updated: Jun 6, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Camera-view supervision for bird's-eye-view semantic segmentation.

Bowen Yang¹, LinLin Yu¹, Feng Chen¹

¹AI Safety Laboratory, Department of Computer Science, The University of Texas at Dallas, Richardson, TX, United States.

Frontiers in Big Data

|December 2, 2024

Summary

This summary is machine-generated.

This study introduces a new method for Bird's-eye-view Semantic Segmentation (BEVSS) in autonomous vehicles. By supervising feature extraction with depth and segmentation data, it achieves more accurate projections and improved vehicle segmentation.

Keywords:

autonomous driving (AD)birds-eye-view nuScenes dataset perception segmentation supervision

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

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Published on: January 18, 2020

Area of Science:

Computer Vision
Robotics
Autonomous Systems

Background:

Bird's-eye-view Semantic Segmentation (BEVSS) is critical for autonomous vehicle perception.
Existing end-to-end methods suffer from indirectly supervised, inaccurate camera-to-BEV projections.

Purpose of the Study:

To develop a novel method for improving BEVSS accuracy.
To enhance feature extraction and camera-to-BEV projection quality.

Main Methods:

Supervising feature extraction with camera-view depth and segmentation information.
Evaluating the proposed model on the nuScenes dataset.

Main Results:

Achieved a 3.8% improvement in Intersection-over-Union (IoU) for vehicle segmentation.
Reduced depth error by 30-fold compared to baseline methods.
Maintained competitive inference speeds of 32 FPS.

Conclusions:

The proposed method offers more accurate and reliable BEVSS for real-time autonomous driving.
Directly supervising feature extraction enhances projection quality.
This approach contributes to safer and more efficient autonomous systems.