Summary of Evaluating the Effectiveness Of Video Anomaly Detection in the Wild: Online Learning and Inference For Real-world Deployment, by Shanle Yao et al.
Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment
by Shanle Yao, Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel video anomaly detection (VAD) framework is proposed to tackle the challenges of real-life scenarios, such as dynamic human actions, environmental variations, and domain shifts. The framework employs online learning to continuously update models with streaming data from novel environments, mirroring actual world challenges. Three state-of-the-art VAD algorithms based on pose analysis are evaluated in this setting, focusing on their adaptability across different domains. The results show that the online learning approach allows a model to preserve 89.39% of its original effectiveness compared to its offline-trained counterpart. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video anomaly detection helps identify unusual activities in videos and can be used for surveillance or healthcare purposes. Researchers often test models using traditional methods, but these don’t account for real-life scenarios. Online learning is a way to adapt models to new information continuously. This paper looks at how well current VAD algorithms work when updated with new data from different environments. The goal is to see if models can keep working accurately in new situations. |
Keywords
» Artificial intelligence » Anomaly detection » Online learning
