超越帧的烹饪：厨房中的立体事件相机数据集
Cooking beyond Frames: A Stereo Event Camera Dataset in the Kitchen
- Delft University of Technology（代尔夫特理工大学）
- STMicroelectronics（意法半导体）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文推出EventKitchen数据集，以第一人称视角从10名参与者在13个厨房中收集5.5小时的烹饪相关多传感器数据，训练基线模型完成动作识别等任务，为神经形态视觉提供新基准。
AI中文摘要：
事件相机（也称为神经形态相机）近年来因高时间分辨率、高动态范围和低功耗而受到广泛关注。尽管神经形态视觉领域的许多研究和数据集都集中在汽车和无人机应用上，但以人类为中心的日常生活场景仍严重缺乏代表性，尽管这些场景对于开发和测试基于事件的感知系统至关重要。此外，现有的少数基于事件的人类活动数据集通常是通过脚本化的人类动作记录的，这限制了它们捕捉自然人类行为的能力。在本文中，我们推出EventKitchen，这是一个用于厨房人类烹饪活动的大规模立体事件相机基准数据集。EventKitchen是以第一人称视角从10名参与者在13个不同厨房中收集的，参与者佩戴带有多个传感器的头盔，自然地进行烹饪活动，无任何脚本化动作。该数据集包含5.5小时的立体事件记录，以及同步的RGB、深度和IMU数据。我们提供了10762个动作片段和13482个边界框的人工标注。我们在EventKitchen上训练基线模型，以执行多个基于事件的任务，包括动作识别、目标检测和立体深度估计。通过捕捉自然的现实世界人类活动，EventKitchen为神经形态视觉建立了超越自动驾驶的具有挑战性的基准。
英文摘要：
Event cameras, also known as neuromorphic cameras, have gained significant attention in recent years due to their high temporal resolution, high dynamic range, and low power consumption. While many studies and datasets in neuromorphic vision have focused on automotive and drone applications, human-centric daily-life scenarios remain largely underrepresented, despite their importance for developing and benchmarking event-based perception systems. Moreover, the few existing event-based human activity datasets are typically recorded with scripted human actions, limiting their ability to capture natural human behaviors. In this paper, we introduce EventKitchen, a large-scale stereo event camera benchmark dataset of human cooking activities in the kitchen. EventKitchen is egocentrically collected from 10 participants in 13 diverse kitchens, where the participants wear a helmet with multiple sensors and naturally perform cooking activities, without any scripted actions. EventKitchen comprises 5.5 hours of stereo event recordings with synchronized RGB, depth, and IMU data. We provide human annotations for 10,762 action segments and 13,482 bounding boxes. We train baseline models on EventKitchen to perform multiple event-based tasks, including action recognition, object detection, and stereo depth estimation. By capturing natural, real-world human activities, EventKitchen establishes a challenging benchmark for neuromorphic vision beyond autonomous driving.
