Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows

arXiv:2404.17381v1 Announce Type: new
Abstract: We introduce the task of human action anomaly detection (HAAD), which aims to identify anomalous motions in an unsupervised manner given only the pre-determined normal category of training action samples. Compared to prior human-related anomaly detection tasks which primarily focus on unusual events from videos, HAAD involves the learning of specific action labels to recognize semantically anomalous human behaviors. To address this task, we propose a normalizing flow (NF)-based detection framework where the sample likelihood …

abstract anomaly anomaly detection arxiv cs.cv detection events focus human identify normal prior samples tasks training type unsupervised videos