  Pose-based anomaly detection is a video-analysis technique for detecting
anomalous events or behaviors by examining human pose extracted from the video
frames. Utilizing pose data alleviates privacy and ethical issues. Also,
computation-wise, the complexity of pose-based models is lower than pixel-based
approaches. However, it introduces more challenges, such as noisy skeleton
data, losing important pixel information, and not having enriched enough
features. These problems are exacerbated by a lack of anomaly detection
datasets that are good enough representatives of real-world scenarios. In this
work, we analyze and quantify the characteristics of two well-known video
anomaly datasets to better understand the difficulties of pose-based anomaly
detection. We take a step forward, exploring the discriminating power of pose
and trajectory for video anomaly detection and their effectiveness based on
context. We believe these experiments are beneficial for a better comprehension
of pose-based anomaly detection and the datasets currently available. This will
aid researchers in tackling the task of anomaly detection with a more lucid
perspective, accelerating the development of robust models with better
performance.
