论文标题
正常的人体姿势异常检测流
Normalizing Flows for Human Pose Anomaly Detection
论文作者
论文摘要
视频异常检测是一个不适的问题，因为它依赖于许多参数，例如外观，姿势，摄像头，背景等。我们将问题提炼成对人姿势的异常检测，从而降低了滋扰参数的风险，例如影响结果的外观。仅专注于姿势也具有减少对不同少数群体的偏见的附带益处。我们的模型直接在人体姿势图序列上起作用，并且非常轻巧（〜1K参数），能够在任何能够以可忽略的其他资源来运行姿势估计的机器上运行。我们利用高度紧凑的姿势表示在标准化框架中，我们扩展了以应对时空姿势数据的独特特征，并在此用例中显示出其优势。该算法是相当笼统的，只能处理一个正常示例的训练数据，以及由标记为正常和异常示例的监督环境。我们报告了两个异常检测基准的最新结果 - 无监督的上海数据集和最近的监督UBNormal数据集。
Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more. We distill the problem to anomaly detection of human pose, thus decreasing the risk of nuisance parameters such as appearance affecting the result. Focusing on pose alone also has the side benefit of reducing bias against distinct minority groups. Our model works directly on human pose graph sequences and is exceptionally lightweight (~1K parameters), capable of running on any machine able to run the pose estimation with negligible additional resources. We leverage the highly compact pose representation in a normalizing flows framework, which we extend to tackle the unique characteristics of spatio-temporal pose data and show its advantages in this use case. The algorithm is quite general and can handle training data of only normal examples as well as a supervised setting that consists of labeled normal and abnormal examples. We report state-of-the-art results on two anomaly detection benchmarks - the unsupervised ShanghaiTech dataset and the recent supervised UBnormal dataset.
