An Attribute-based Method for Video Anomaly Detection
This addresses crime prevention and homeland security by improving detection of suspicious events in videos, though it appears incremental as it combines simple attributes with existing deep representations.
The paper tackled video anomaly detection by proposing an attribute-based method using velocity and pose representations, achieving state-of-the-art performance with AUROC scores of 99.1%, 93.7%, and 85.9% on Ped2, Avenue, and ShanghaiTech datasets.
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.