Summary of A Scalable and Generalized Deep Learning Framework For Anomaly Detection in Surveillance Videos, by Sabah Abdulazeez Jebur et al.
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos
by Sabah Abdulazeez Jebur, Khalid A. Hussein, Haider Kadhim Hoomod, Laith Alzubaidi, Ahmed Ali Saihood, YuanTong Gu
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Anomaly detection in videos is crucial for addressing real-world issues like violence, shoplifting, and vandalism. Despite deep learning’s (DL) impressive performance in this area, existing approaches struggle to apply DL models across diverse anomaly tasks without extensive retraining. This study addresses this limitation by introducing a novel DL framework consisting of transfer learning, model fusion, and multi-task classification. The framework generalizes well across multiple tasks without requiring retraining from scratch for each new task. Empirically, it achieves high accuracy on various datasets, including RLVS (violence detection), UCF (shoplifting detection), and a combination of both using a single classifier. Additionally, the study utilizes explainability tools to ensure robustness and fairness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to detect bad behavior like violence or shoplifting in videos. It’s hard because there are many types of activities and lots of noise. Deep learning can help, but current methods need a lot of work to make them work for different tasks. This study makes it easier by creating a new way to use deep learning that works well across multiple tasks without needing to start over each time. The method is tested on several datasets and does very well, even when used on a new dataset it hasn’t seen before. The researchers also made sure their method is fair and doesn’t make mistakes based on certain factors. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Deep learning » Multi task » Transfer learning
