论文标题
视频异常检测的基于属性的方法
An Attribute-based Method for Video Anomaly Detection
论文作者
论文摘要
视频异常检测（VAD）确定了视频中的可疑事件，这对于预防犯罪和国土安全至关重要。在本文中，我们提出了一种依赖基于属性的表示的简单但高效的VAD方法。我们方法的基本版本以其速度和姿势表示每个对象，并根据密度估计计算异常得分。令人惊讶的是，这种简单的表示足以实现最常用的VAD数据集上的上学性能。将我们的基于属性的表示与现成的，预处理的深层表示相结合，以$ 99.1 \％，93.7 \％$和$ 85.9 \％$ $ AUROC在PED2，Avenue和Shanghaitech上产生最新的表现。
Video anomaly detection (VAD) identifies suspicious events in videos, which is critical for crime prevention and homeland security. In this paper, we propose a simple but highly effective VAD method that relies on attribute-based representations. The base version of our method represents every object by its velocity and pose, and computes anomaly scores by density estimation. Surprisingly, this simple representation is sufficient to achieve state-of-the-art performance in ShanghaiTech, the most commonly used VAD dataset. Combining our attribute-based representations with an off-the-shelf, pretrained deep representation yields state-of-the-art performance with a $99.1\%, 93.7\%$, and $85.9\%$ AUROC on Ped2, Avenue, and ShanghaiTech, respectively.
