Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

463
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
463
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.6K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
2.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Information Theory and Coding for Image and Video Processing.

Entropy (Basel, Switzerland)·2026
Same author

Reliable QoE Prediction in IMVCAs Using an LMM-Based Agent.

Sensors (Basel, Switzerland)·2025
Same author

Improved Perceptual Quality of Traffic Signs and Lights for the Teleoperation of Autonomous Vehicle Remote Driving via Multi-Category Region of Interest Video Compression.

Entropy (Basel, Switzerland)·2025
Same author

Estimating QoE from Encrypted Video Conferencing Traffic.

Sensors (Basel, Switzerland)·2025
Same author

Automatic Root Length Estimation from Images Acquired In Situ without Segmentation.

Plant phenomics (Washington, D.C.)·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Mar 29, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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

9.7K

Off-Road Autonomous Vehicle Semantic Segmentation and Spatial Overlay Video Assembly.

Itai Dror1, Omer Aviv1, Ofer Hadar1

  • 1School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel three-part solution for off-road autonomous vehicles, enhancing perception and data compression. The findings improve navigation in challenging environments and enable efficient remote operation.

Keywords:
confusion-aware losscross-domain generalizationoff-road autonomous vehiclessemantic segmentationsemantic video compressionspatial overlay videovideo encoding

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.7K

Related Experiment Videos

Last Updated: Mar 29, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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

9.7K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.7K

Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Data Compression

Background:

  • Autonomous systems require robust perception for unstructured environments.
  • Off-road autonomy faces challenges like dynamic terrain and limited communication.
  • Existing solutions struggle with the complexity of off-road navigation.

Purpose of the Study:

  • To develop a comprehensive solution for off-road autonomous vehicle perception and operation.
  • To address the unique challenges posed by unstructured, off-road environments.
  • To enable efficient real-time remote operation in bandwidth-constrained scenarios.

Main Methods:

  • Curated a large-scale off-road dataset for realistic training and model generalization.
  • Proposed a Confusion-Aware Loss (CAL) to improve semantic segmentation accuracy.
  • Introduced a spatial overlay video encoding scheme for efficient data transmission.

Main Results:

  • CAL improved segmentation mIoU from 68.66% to 70.06% on off-road data.
  • Achieved cross-domain mIoU gains of up to 0.49% on the Cityscapes dataset.
  • Video encoding improved PSNR by up to +5 dB and VMAF by up to +40 points.

Conclusions:

  • The integrated three-part solution enhances off-road autonomous vehicle perception and efficiency.
  • The developed methods provide a robust framework for challenging, unstructured environments.
  • This research facilitates reliable real-time remote operation under bandwidth constraints.