{"ID":2874589,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.04117","arxiv_id":"2509.04117","title":"DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset","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; and (2) real-world JAAD dash-cam videos 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 labels (crossing vs. not crossing). We also provide raw AEDAT 2.0/AEDAT 4.0 event files and AVI DVS video files and metadata for flexible re-processing. Baseline spiking neural networks (SNNs) using SpikingJelly illustrate dataset usability and reveal a sim-to-real gap, motivating domain adaptation and multimodal fusion. DVS-PedX aims to accelerate research in event-based pedestrian safety, intention prediction, and neuromorphic perception.","short_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 analys...","url_abs":"https://arxiv.org/abs/2509.04117","url_pdf":"https://arxiv.org/pdf/2509.04117v1","authors":"[\"Mustafa Sakhai\",\"Kaung Sithu\",\"Min Khant Soe Oke\",\"Maciej Wielgosz\"]","published":"2025-09-04T11:30:29Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"LoRA\"]","has_code":false}