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

• URL: http://arxiv.org/abs/2509.04117v1
• Date: Thu, 04 Sep 2025 11:30:29 GMT
• Title: DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset
• Abstract summary: Event cameras like Dynamic Vision Sensors (DVS) report micro-timed brightness changes instead of full frames.<n>DVS-PedX is a neuromorphic dataset designed for pedestrian detection and crossing-intention analysis in normal and adverse weather conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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