Summary of Deep Learning For Video Anomaly Detection: a Review, by Peng Wu et al.
Deep Learning for Video Anomaly Detection: A Review
by Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Peng Wang, Yanning Zhang
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive survey on video anomaly detection (VAD) methods, covering five categories: semi-supervised, weakly supervised, fully supervised, unsupervised, and open-set supervised VAD. The authors focus on deep learning-based approaches, which have improved the generalization ability of detection algorithms and expanded application scenarios. The review highlights the characteristics of different methods, their performance comparisons, public datasets, open-source codes, and evaluation metrics. This comprehensive survey aims to address the limitations of past reviews by covering a broader range of VAD methods and pre-trained large models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video anomaly detection (VAD) is trying to find unusual behaviors or events in videos. This task has been getting better over time, thanks to advances in computer vision. Many different approaches have been developed using deep learning, which helps improve the ability of algorithms to detect anomalies and makes them useful for many applications. This paper looks at all these methods, categorizing them into five types: semi-supervised, weakly supervised, fully supervised, unsupervised, and open-set supervised VAD. The authors also discuss recent advances in using large pre-trained models for VAD. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » Generalization » Semi supervised » Supervised » Unsupervised
