  This study investigates unsupervised anomaly action recognition, which
identifies video-level abnormal-human-behavior events in an unsupervised manner
without abnormal samples, and simultaneously addresses three limitations in the
conventional skeleton-based approaches: target domain-dependent DNN training,
robustness against skeleton errors, and a lack of normal samples. We present a
unified, user prompt-guided zero-shot learning framework using a target
domain-independent skeleton feature extractor, which is pretrained on a
large-scale action recognition dataset. Particularly, during the training phase
using normal samples, the method models the distribution of skeleton features
of the normal actions while freezing the weights of the DNNs and estimates the
anomaly score using this distribution in the inference phase. Additionally, to
increase robustness against skeleton errors, we introduce a DNN architecture
inspired by a point cloud deep learning paradigm, which sparsely propagates the
features between joints. Furthermore, to prevent the unobserved normal actions
from being misidentified as abnormal actions, we incorporate a similarity score
between the user prompt embeddings and skeleton features aligned in the common
space into the anomaly score, which indirectly supplements normal actions. On
two publicly available datasets, we conduct experiments to test the
effectiveness of the proposed method with respect to abovementioned
limitations.
