提出分层开放集分类器，可识别未知动作并定位其所属类别层级。
Hi-OSCAR: Hierarchical Open-set Classifier for Human Activity Recognition
- 构建动作类别层次结构，利用层级关系提升分类逻辑性。
- 在十九类动作上实现顶尖准确率，并有效识别未知动作。
- 新数据集NFI_FARED公开可用，涵盖生活、通勤等多场景动作。
人体活动识别（HAR）中，真实生活中涉及的动作远超标注数据集所能覆盖的范围。无法处理未见动作会严重影响分类器可靠性。此外，不同动作间存在显著重叠或包含子动作的情况。为此，本文构建动作类别的层次结构，提出Hi-OSCAR：一种分层开放集分类器，可在保持当前最佳准确率的同时，有效拒绝未知动作。该方法不仅能实现开放集分类，还能将未知动作定位到最近的内部节点，提供超越“已知/未知”二元判断的语义信息。为推动此类研究，本文构建了新数据集NFI_FARED，包含多名受试者在日常生活、通勤及快速运动等场景下执行的十九类动作，数据完全公开可下载。
原文摘要 · Abstract (English)
Within Human Activity Recognition (HAR), there is an insurmountable gap between the range of activities performed in life and those that can be captured in an annotated sensor dataset used in training. Failure to properly handle unseen activities seriously undermines any HAR classifier's reliability. Additionally within HAR, not all classes are equally dissimilar, some significantly overlap or encompass other sub-activities. Based on these observations, we arrange activity classes into a structured hierarchy. From there, we propose Hi-OSCAR: a Hierarchical Open-set Classifier for Activity Recognition, that can identify known activities at state-of-the-art accuracy while simultaneously rejecting unknown activities. This not only enables open-set classification, but also allows for unknown classes to be localized to the nearest internal node, providing insight beyond a binary "known/unknown" classification. To facilitate this and future open-set HAR research, we collected a new dataset: NFI_FARED. NFI_FARED contains data from multiple subjects performing nineteen activities from a range of contexts, including daily living, commuting, and rapid movements, which is fully public and available for download.
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