Understanding the Challenges and Opportunities of Pose-based Anomaly Detection

Ghazal Alinezhad Noghre, Armin Danesh Pazho, Vinit Katariya, Hamed Tabkhi

Abstract: Pose-based anomaly detection is a video-analysis technique for detecting anomalous events or behaviors by examining human pose extracted from the video frames. Human anomaly detection plays a crucial role in various applications, such as smart cities and intelligent surveillance systems, for the safety of public environments. Utilizing pose data alleviates the privacy and ethical issues while reducing computational complexity compared to 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 the scarcity of high-quality anomaly detection datasets that are good enough representatives of real-world scenarios. In this work, we analyze and quantify the characteristics of two 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. Consequently, our findings will provide valuable insights into the benefits and limitations of pose-based approaches for anomaly detection.