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 is effectively leveraged to indicate anomalies. As action anomalies often occur in some specific body parts, in addition to the full-body action feature learning, we incorporate extra encoding streams into our framework for a finer modeling of body subsets. Our framework is thus multi-level to jointly discover global and local motion anomalies. Furthermore, to show awareness of the potentially jittery data during recording, we resort to discrete cosine transformation by converting the action samples from the temporal to the frequency domain to mitigate the issue of data instability. Extensive experimental results on two human action datasets demonstrate that our method outperforms the baselines formed by adapting state-of-the-art human activity AD approaches to our task of HAAD.
Abstract (translated)
我们提出了人类动作异常检测（HAAD）任务，旨在在不经监督的情况下，根据预先确定的正常分类来检测异常运动。与主要关注从视频中寻找异常事件的先前人类相关异常检测任务相比，HAAD涉及学习具体的动作标签，以识别语义异常的人类行为。为解决这个任务，我们提出了一个基于归一化流（NF）的检测框架，有效利用样本可能性来指示异常。由于动作异常通常发生在某些特定身体部位，因此我们在框架中引入额外的编码流以进行更精细的身体子集建模。因此，我们的框架是多层级的，可以共同发现全局和局部运动异常。此外，为了展示在录制过程中意识到的数据抖动，我们通过将动作样本从时间域转换到频率域的离散余弦变换来减轻数据不稳定性。在两个人类动作数据集上的广泛实验结果表明，我们的方法优于将最先进的的人类活动AD方法适应为HAAD任务所构成的基线。
URL
https://arxiv.org/abs/2404.17381
