Video Anomaly Detection (VAD) plays a crucial role in modern surveillance
systems, aiming to identify various anomalies in real-world situations.
However, current benchmark datasets predominantly emphasize simple,
single-frame anomalies such as novel object detection. This narrow focus
restricts the advancement of VAD models. In this research, we advocate for an
expansion of VAD investigations to encompass intricate anomalies that extend
beyond conventional benchmark boundaries. To facilitate this, we introduce two
datasets, HMDB-AD and HMDB-Violence, to challenge models with diverse
action-based anomalies. These datasets are derived from the HMDB51 action
recognition dataset. We further present Multi-Frame Anomaly Detection (MFAD), a
novel method built upon the AI-VAD framework. AI-VAD utilizes single-frame
features such as pose estimation and deep image encoding, and two-frame
features such as object velocity. They then apply a density estimation
algorithm to compute anomaly scores. To address complex multi-frame anomalies,
we add a deep video encoding features capturing long-range temporal
dependencies, and logistic regression to enhance final score calculation.
Experimental results confirm our assumptions, highlighting existing models
limitations with new anomaly types. MFAD excels in both simple and complex
anomaly detection scenarios.

视频异常检测（VAD）在现代监控系统中扮演着关键角色，旨在识别真实情境中的各种异常。然而，当前基准数据集主要强调简单的单帧异常，如新颖物体检测，这种狭窄的焦点限制了 VAD 模型的进展。在这项研究中，我们主张扩展 VAD 研究，涵盖超越传统基准范围的复杂异常。为此，我们引入了两个数据集：HMDB-AD 和 HMDB-Violence，以挑战具有多样化基于动作的异常的模型。这些数据集源于 HMDB51 动作识别数据集。我们进一步介绍了一种新方法 Multi-Frame Anomaly Detection（MFAD），它建立在 AI-VAD 框架之上。AI-VAD 利用单帧特征，如姿势估计和深度图像编码，以及双帧特征，如物体速度。然后，他们应用密度估计算法计算异常分数。为了解决复杂的多帧异常，我们添加了捕捉长期时态依赖性的深度视频编码特征和逻辑回归以增强最终分数计算。实验结果证实了我们的假设，并突显出现有模型对新异常类型的限制。在简单和复杂的异常检测场景中，MFAD 表现出色。