Abstract
Reliable human orientation estimation (HOE) is critical for autonomous agents to understand human intention and perform human-robot interaction (HRI) tasks. Great progress has been made in HOE under full observation. However, the existing methods easily make a wrong prediction under partial observation and give it an unexpectedly high probability. To solve the above problems, this study first develops a method that estimates orientation from the visible joints of a target person so that it is able to handle partial observation. Subsequently, we introduce a confidence-aware orientation estimation method, enabling more accurate orientation estimation and reasonable confidence estimation under partial observation. The effectiveness of our method is validated on both public and custom-built datasets, and it showed great accuracy and reliability improvement in partial observation scenarios. In particular, we show in real experiments that our method can benefit the robustness and consistency of the robot person following (RPF) task.
Abstract (translated)
可靠的人体方向估计（HOE）对于自主机器人来说理解人类意图并执行人机交互（HRI）任务至关重要。在完全观察的情况下，HOE取得了很大的进展。然而，现有的方法在部分观察时很容易做出错误的预测，并且给出了意外高的概率。为了解决上述问题，本研究首先开发了一种从目标人物的可视关节估计方向的方法，使其能够处理部分观察。接着，我们引入了一种基于信心的方向估计方法，使得在部分观察的情况下进行更准确的方向估计和合理的自信估计。我们方法的成效在公开和定制数据集上都被验证，并在部分观察场景中取得了很大精度和可靠性提升。特别地，在实际实验中，我们证明了我们的方法可以提高机器人人们在（RPF）任务中的鲁棒性和一致性。
URL
https://arxiv.org/abs/2404.14139
