论文标题
Meccano数据集：了解类似工业领域中以自我为中心视频的人类对象相互作用
The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain
论文作者
论文摘要
可穿戴摄像机可以收集与世界互动的人类互动的图像和视频。尽管已经对第三人称视力进行了彻底研究人类对象的相互作用，但在以自我为中心的环境和工业场景中，该问题已经研究了。为了填补这一空白，我们介绍了Meccano，这是以自我为中心视频的第一个数据集研究类似工业的环境中的人类对象相互作用。 Meccano已被20名参与者收购，他们被要求建立摩托车模型，为此他们必须与微小的物体和工具进行互动。该数据集已被明确标记为从以自我为中心的角度识别人类对象相互作用的任务。具体而言，每种相互作用均已在时间（带有动作段）和空间（带有活动对象边界框）上标记。通过提出的数据集，我们研究了四个不同的任务，包括1）动作识别，2）主动对象检测，3）主动对象识别和4）以Egentric的人类对象相互作用检测，这是标准人类对象交互检测任务的重新审视版本。基线结果表明，Meccano数据集是研究类似工业的场景中以自我为中心的人类对象相互作用的具有挑战性的基准。我们在https://iplab.dmi.unict.it/meccano上公开发布数据集。
Wearable cameras allow to collect images and videos of humans interacting with the world. While human-object interactions have been thoroughly investigated in third person vision, the problem has been understudied in egocentric settings and in industrial scenarios. To fill this gap, we introduce MECCANO, the first dataset of egocentric videos to study human-object interactions in industrial-like settings. MECCANO has been acquired by 20 participants who were asked to build a motorbike model, for which they had to interact with tiny objects and tools. The dataset has been explicitly labeled for the task of recognizing human-object interactions from an egocentric perspective. Specifically, each interaction has been labeled both temporally (with action segments) and spatially (with active object bounding boxes). With the proposed dataset, we investigate four different tasks including 1) action recognition, 2) active object detection, 3) active object recognition and 4) egocentric human-object interaction detection, which is a revisited version of the standard human-object interaction detection task. Baseline results show that the MECCANO dataset is a challenging benchmark to study egocentric human-object interactions in industrial-like scenarios. We publicy release the dataset at https://iplab.dmi.unict.it/MECCANO.
