Pose estimation commonly refers to computer vision methods that recognize people's body postures in images or videos. With recent advancements in deep learning, we now have compelling models to tackle the problem in real-time. Since these models are usually designed for human images, one needs to adapt existing models to work on other creatures, including robots. This paper examines different state-of-the-art pose estimation models and proposes a lightweight model that can work in real-time on humanoid robots in the RoboCup Humanoid League environment. Additionally, we present a novel dataset called the HumanoidRobotPose dataset. The results of this work have the potential to enable many advanced behaviors for soccer-playing robots.
翻译:Pose 估计通常指在图像或视频中识别人的身体姿势的计算机视觉方法。 随着最近深层学习的进步, 我们现在有了令人信服的模型来实时解决这个问题。 由于这些模型通常为人类图像设计, 因此需要调整现有的模型来适用于其他生物, 包括机器人。 本文考察了不同的最先进的模型, 并提出了一个轻量级模型, 可以实时操作机器人在机器人人类联盟环境中的人体机器人。 此外, 我们展示了一个新的数据集, 称为人类机器人数据集。 这项工作的结果有可能使足球机器人的许多先进行为成为可能 。