  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.
