Understanding the Challenges and Opportunities of Pose-based Anomaly Detection

• URL: http://arxiv.org/abs/2303.05463v1
• Date: Thu, 9 Mar 2023 18:09:45 GMT
• Title: Understanding the Challenges and Opportunities of Pose-based Anomaly Detection
• Authors: Ghazal Alinezhad Noghre, Armin Danesh Pazho, Vinit Katariya, Hamed Tabkhi
• Abstract summary: Pose-based anomaly detection is a video-analysis technique for detecting anomalous events or behaviors by examining human pose extracted from the video frames. 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 believe these experiments are beneficial for a better comprehension of pose-based anomaly detection and the datasets currently available.
• Score: 2.924868086534434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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.

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