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

Pedigree Analysis01:35

Pedigree Analysis

78.9K
Overview
78.9K

You might also read

Related Articles

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

Sort by
Same author

Using Super-Resolution for Enhancing Visual Perception and Segmentation Performance in Veterinary Cytology.

Life (Basel, Switzerland)·2024
Same author

Mapping Neural Networks to FPGA-Based IoT Devices for Ultra-Low Latency Processing.

Sensors (Basel, Switzerland)·2019
Same author

Protection of Superconducting Industrial Machinery Using RNN-Based Anomaly Detection for Implementation in Smart Sensor.

Sensors (Basel, Switzerland)·2018
See all related articles

Related Experiment Video

Updated: May 6, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K

DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset.

Mustafa Sakhai1, Kaung Sithu2, Min Khant Soe Oke2

  • 1Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, 30-059, Krakow, Poland. msakhai@agh.edu.pl.

Scientific Data
|March 5, 2026
PubMed
Summary
This summary is machine-generated.

A new dataset, DVS-PedX, uses event cameras for pedestrian detection and intention analysis in various conditions. Baseline Spiking Neural Network models show promising results, highlighting a need for domain adaptation in event-based perception research.

More Related Videos

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.2K
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

5.4K

Related Experiment Videos

Last Updated: May 6, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.2K
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

5.4K

Area of Science:

  • Neuromorphic Engineering
  • Computer Vision
  • Robotics

Background:

  • Event cameras, such as Dynamic Vision Sensors (DVS), capture brightness changes asynchronously, offering advantages in low latency, high dynamic range, and motion robustness compared to traditional frame-based cameras.
  • Pedestrian detection and intention prediction are critical for autonomous systems, especially in challenging environmental conditions like adverse weather and varying lighting.

Purpose of the Study:

  • Introduce DVS-PedX, a novel neuromorphic dataset for pedestrian detection and crossing-intention analysis.
  • Facilitate research in event-based perception for enhanced pedestrian safety and intention prediction systems.
  • Provide a comprehensive resource for studying the sim-to-real gap in event-based vision.

Main Methods:

  • DVS-PedX comprises two sources: synthetic event streams from CARLA simulator (198 sequences) and real-world JAAD dash-cam videos converted to event streams using v2e (346 clips).
  • Data includes paired RGB frames, DVS event frames (33 ms accumulations), and binary labels for crossing events.
  • Baseline experiments utilized Spiking Neural Networks (SNNs) implemented with SpikingJelly for performance evaluation.

Main Results:

  • Spiking Neural Networks achieved an F1-score of 86.37% on the synthetic validation set.
  • The study identified a significant sim-to-real gap, indicating challenges in transferring models trained on synthetic data to real-world scenarios.
  • The dataset provides raw event files (AEDAT 2.0/4.0), DVS video files, and metadata for flexible data processing and model development.

Conclusions:

  • DVS-PedX is a valuable resource for advancing event-based pedestrian safety and intention prediction research.
  • The observed sim-to-real gap underscores the importance of domain adaptation and multimodal fusion techniques for robust event-based perception.
  • The dataset aims to accelerate the development of neuromorphic perception systems for autonomous applications.