  Human action recognition is a crucial task in computer vision systems.
However, in real-world scenarios, human actions often fall outside the
distribution of training data, requiring a model to both recognize
in-distribution (ID) actions and reject out-of-distribution (OOD) ones. Despite
its importance, there has been limited research on OOD detection in human
actions. Existing works on OOD detection mainly focus on image data with RGB
structure, and many methods are post-hoc in nature. While these methods are
convenient and computationally efficient, they often lack sufficient accuracy
and fail to consider the presence of OOD samples. To address these challenges,
we propose a novel end-to-end skeleton-based model called Action-OOD,
specifically designed for OOD human action detection. Unlike some existing
approaches that may require prior knowledge of existing OOD data distribution,
our model solely utilizes in-distribution (ID) data during the training stage,
effectively mitigating the overconfidence issue prevalent in OOD detection. We
introduce an attention-based feature fusion block, which enhances the model's
capability to recognize unknown classes while preserving classification
accuracy for known classes. Further, we present a novel energy-based loss
function and successfully integrate it with the traditional cross-entropy loss
to maximize the separation of data distributions between ID and OOD. Through
extensive experiments conducted on NTU-RGB+D 60, NTU-RGB+D 120, and
Kinetics-400 datasets, we demonstrate the superior performance of our proposed
approach compared to state-of-the-art methods. Our findings underscore the
effectiveness of classic OOD detection techniques in the context of
skeleton-based action recognition tasks, offering promising avenues for future
research in this field. Code will be available at:
https://github.com/YilliaJing/Action-OOD.git.
