Summary of Towards Adaptive Human-centric Video Anomaly Detection: a Comprehensive Framework and a New Benchmark, by Armin Danesh Pazho et al.
Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark
by Armin Danesh Pazho, Shanle Yao, Ghazal Alinezhad Noghre, Babak Rahimi Ardabili, Vinit Katariya, Hamed Tabkhi
First submitted to arxiv on: 26 Aug 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 The paper proposes a novel approach to Human-centric Video Anomaly Detection (VAD), which aims to identify human behaviors that deviate from normal. The authors acknowledge the challenges of VAD, including dataset scarcity and ethical constraints. To address these issues, they introduce the HuVAD dataset and the Unsupervised Continual Anomaly Learning (UCAL) framework. UCAL enables incremental learning, allowing models to adapt over time and bridge the gap between traditional training and real-world deployment. The HuVAD dataset prioritizes privacy by providing de-identified annotations and includes seven indoor/outdoor scenes with over 5x more pose-annotated frames than previous datasets. The authors demonstrate the effectiveness of UCAL-enhanced models, achieving superior performance in 82.14% of cases, setting a new state-of-the-art (SOTA). The dataset is available at https://github.com/TeCSAR-UNCC/HuVAD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to find weird human behaviors in videos that are not normal. It’s hard because there aren’t many good datasets and we have to be careful not to invade people’s privacy. The authors created a new dataset called HuVAD with lots of different scenes and made a special way for computers to learn about anomalies over time. They tested it and found that their method worked really well, beating the previous best results by a lot. You can find more information about this project on GitHub. |
Keywords
» Artificial intelligence » Anomaly detection » Unsupervised
