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Related Concept Videos

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Depth Perception and Spatial Vision

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.
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Related Experiment Video

Updated: Jun 16, 2026

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

A pedestrian perspective street view dataset for prosthetic vision simulation.

Ziyang Fan1, Ying Zhao1, Lin Gan1

  • 1Inner Mongolia University of Science & Technology, Baotou 014010, China.

Data in Brief
|June 15, 2026
PubMed
Summary

A new Pedestrian Perspective Street View Dataset (PPSVD) offers realistic urban scenes for simulated prosthetic vision research. Models trained on PPSVD show improved segmentation accuracy, especially in complex environments.

Keywords:
Image datasetInstance segmentation for street scenePedestrian perspectiveSimulated prosthetic visionYOLO Model

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Area of Science:

  • Computer Vision
  • Robotics
  • Human-Computer Interaction

Background:

  • Traditional street view datasets lack pedestrian-centric perspectives, limiting realism for simulated prosthetic vision research.
  • Existing datasets do not adequately capture the complexities of urban environments from a pedestrian's viewpoint.

Purpose of the Study:

  • To introduce the Pedestrian Perspective Street View Dataset (PPSVD) for enhanced simulated prosthetic vision research.
  • To create a dataset that realistically represents street scenes from a pedestrian's perspective, addressing limitations of vehicle-mounted data.

Main Methods:

  • Collected 1200 high-quality, instance-level annotated images from real-world street scenes.
  • Ensured the dataset covers 16 common urban object categories, including pedestrians, vehicles, and traffic infrastructure.
  • Developed the Pedestrian Perspective Street View Dataset (PPSVD) to simulate complex urban environments.

Main Results:

  • Models trained on PPSVD significantly outperformed those trained on general datasets in segmentation accuracy.
  • Performance improvements were particularly pronounced in complex scenes, with small targets, and occluded situations.
  • PPSVD demonstrated effectiveness across diverse model sizes and structures, confirming its broad applicability.

Conclusions:

  • PPSVD provides a more realistic data resource for simulated prosthetic vision experiments.
  • The dataset enhances the performance of computer vision models in pedestrian-centric urban scenarios.
  • PPSVD serves as a valuable resource for advancing research in simulated prosthetic vision and related fields.