论文标题
以综合数据为中心的人类对象相互作用检测
Egocentric Human-Object Interaction Detection Exploiting Synthetic Data
论文作者
论文摘要
我们考虑在工业背景下检测以自我为中心的人体对象相互作用（EHOI）的问题。由于收集和标记大量的真实图像是具有挑战性的，因此我们提出了一条管道和一种工具，以生成照片真实的合成第一人称视觉（FPV）图像在特定的工业场景中自动标记用于EHOI检测的图像。为了解决EHOI检测的问题，我们提出了一种检测手，场景中的对象的方法，并确定当前参与交互的哪些对象。我们将方法的性能与一组最先进的基线进行了比较。结果表明，使用合成数据集可改善EHOI检测系统的性能，尤其是在很少有实际数据的情况下。为了鼓励对此主题进行研究，我们将在以下URL上公开发布提议的数据集：https：//iplab.dmi.unict.it/ehoi_synth/。
We consider the problem of detecting Egocentric HumanObject Interactions (EHOIs) in industrial contexts. Since collecting and labeling large amounts of real images is challenging, we propose a pipeline and a tool to generate photo-realistic synthetic First Person Vision (FPV) images automatically labeled for EHOI detection in a specific industrial scenario. To tackle the problem of EHOI detection, we propose a method that detects the hands, the objects in the scene, and determines which objects are currently involved in an interaction. We compare the performance of our method with a set of state-of-the-art baselines. Results show that using a synthetic dataset improves the performance of an EHOI detection system, especially when few real data are available. To encourage research on this topic, we publicly release the proposed dataset at the following url: https://iplab.dmi.unict.it/EHOI_SYNTH/.
