Szczegóły publikacji
Opis bibliograficzny
DVS-PedX: synthetic-and-real event-based pedestrian dataset / Mustafa SAKHAI, Kaung Sithu, Min Khant Soe Oke, Maciej WIELGOSZ // Scientific Data [Dokument elektroniczny]. – Czasopismo elektroniczne ; ISSN 2052-4463 . — 2026 — vol. 13 iss. 1 art. no. 614, s. 1–9. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 8–9, Abstr. — Publikacja dostępna online od: 2026-03-06. — M. Wielgosz - dod. afiliacja: Akademickie Centrum Komputerowe CYFRONET AGH
Autorzy (4)
Dane bibliometryczne
| ID BaDAP | 167462 |
|---|---|
| Data dodania do BaDAP | 2026-05-05 |
| Tekst źródłowy | URL |
| DOI | 10.1038/s41597-026-06969-y |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Scientific Data |
Abstract
Event cameras like Dynamic Vision Sensors (DVS) report micro-timed brightness changes instead of full frames, offering low latency, high dynamic range, and motion robustness. DVS-PedX (Dynamic Vision Sensor Pedestrian eXploration) is a neuromorphic dataset designed for pedestrian detection and crossing-intention analysis in normal and adverse weather conditions across two complementary sources: (1) synthetic event streams generated in the CARLA simulator for controlled “approach–cross” scenes under varied weather and lighting, comprising 198 sequences (117 good weather, 81 bad weather) with 178,200 total frames; and (2) real-world JAAD dash-cam videos (346 clips) converted to event streams using the v2e tool, preserving natural behaviors and backgrounds. Each sequence includes paired RGB frames, per-frame DVS “event frames” (33 ms accumulations), and frame-level binary labels (crossing vs. not crossing). We provide raw AEDAT 2.0/AEDAT 4.0 event files, AVI DVS video files, and metadata for flexible re-processing. Baseline experiments using spiking neural networks (SNNs) with SpikingJelly achieve an F1-score of 86.37% on the synthetic validation set, revealing a sim-to-real gap that motivates domain adaptation and multimodal fusion. DVS-PedX aims to accelerate research in event-based pedestrian safety, intention prediction, and neuromorphic perception. © The Author(s) 2026.
