An Attribute-based Method for Video Anomaly Detection

Understand

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.