Human Orientation Estimation under Partial Observation
CoRR(2024)
摘要
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.
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