ENIGMA-360：一种用于工业场景中人类行为理解的自体-外体数据集
ENIGMA-360: An Ego-Exo Dataset for Human Behavior Understanding in Industrial Scenarios
AI总结：
ENIGMA-360数据集旨在通过自体和外体视角提升工业场景中的人类行为理解，提供了时间同步的视频和标注，用于研究和验证现有方法的局限性。
摘要：
从互补的自体（ego）和外体（exo）视角理解人类行为，可以开发支持工人在工业环境中的系统，并提高他们的安全水平。然而，这一领域的进展受到缺乏在真实工业场景中捕捉两种视角的数据集的阻碍。为了解决这一缺口，我们提出了ENIGMA-360，一个新的人体-外体数据集，是在真实工业场景中获取的。该数据集由180个自体和180个外体的程序视频组成，时间同步提供同一场景的互补信息。360视频已用时间和空间注释进行标记，使研究工业领域的人类行为的不同方面成为可能。我们为人类行为理解的三个基础任务提供了基准实验：1）时间动作分割，2）关键步骤识别，3）自体人类-物体交互检测，展示了在这一具有挑战性的场景中现有最先进方法的局限性。这些结果突显了需要新的模型来在真实环境中实现稳健的自体-外体理解的必要性。我们公开发布该数据集及其注释，网址为https://fpv-iplab.github.io/ENIGMA-360/。
英文摘要：
Understanding human behavior from complementary egocentric (ego) and exocentric (exo) points of view enables the development of systems that can support workers in industrial environments and enhance their safety. However, progress in this area is hindered by the lack of datasets capturing both views in realistic industrial scenarios. To address this gap, we propose ENIGMA-360, a new ego-exo dataset acquired in a real industrial scenario. The dataset is composed of 180 egocentric and 180 exocentric procedural videos temporally synchronized offering complementary information of the same scene. The 360 videos have been labeled with temporal and spatial annotations, enabling the study of different aspects of human behavior in industrial domain. We provide baseline experiments for 3 foundational tasks for human behavior understanding: 1) Temporal Action Segmentation, 2) Keystep Recognition and 3) Egocentric Human-Object Interaction Detection, showing the limits of state-of-the-art approaches on this challenging scenario. These results highlight the need for new models capable of robust ego-exo understanding in real-world environments. We publicly release the dataset and its annotations at https://fpv-iplab.github.io/ENIGMA-360/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
