构建合成事件相机行人姿态数据集，助力复杂场景下的行人监测。
SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring
- 用CARLA仿真生成带标注的事件相机行人姿态数据
- 涵盖35万+行人，覆盖多种光照天气与城乡场景
- 验证了模型从仿真到真实数据的泛化能力
事件传感器因其低延迟和高动态范围，成为应对行人与交通监控中复杂场景的有前景方案。但相关场景的数据仍有限。为此，我们提出SEPose——一个基于CARLA模拟器生成的合成事件相机行人姿态估计数据集，专为固定视角的行人感知设计。该数据集包含近35万条带身体关键点标注的行人数据，源自城市、郊区和农村四向交叉口在不同光照与天气条件下的动态视觉传感器模拟。数据覆盖密集与稀疏人流，适用于多人群姿态估计。我们在该数据集上训练了RVT和YOLOv8等先进模型，并在真实事件数据上评估其性能，验证了数据集的模拟到现实（sim-to-real）泛化能力。
原文摘要 · Abstract (English)
Event-based sensors have emerged as a promising solution for addressing challenging conditions in pedestrian and traffic monitoring systems. Their low-latency and high dynamic range allow for improved response time in safety-critical situations caused by distracted walking or other unusual movements. However, the availability of data covering such scenarios remains limited. To address this gap, we present SEPose -- a comprehensive synthetic event-based human pose estimation dataset for fixed pedestrian perception generated using dynamic vision sensors in the CARLA simulator. With nearly 350K annotated pedestrians with body pose keypoints from the perspective of fixed traffic cameras, SEPose is a comprehensive synthetic multi-person pose estimation dataset that spans busy and light crowds and traffic across diverse lighting and weather conditions in 4-way intersections in urban, suburban, and rural environments. We train existing state-of-the-art models such as RVT and YOLOv8 on our dataset and evaluate them on real event-based data to demonstrate the sim-to-real generalization capabilities of the proposed dataset.
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