Summary of Networking Systems For Video Anomaly Detection: a Tutorial and Survey, by Jing Liu et al.
Networking Systems for Video Anomaly Detection: A Tutorial and Survey
by Jing Liu, Yang Liu, Jieyu Lin, Jielin Li, Liang Cao, Peng Sun, Bo Hu, Liang Song, Azzedine Boukerche, Victor C.M. Leung
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 explores the advancement of Automated Video Anomaly Detection (VAD) in smart cities, driven by deep learning and edge computing. VAD has become a crucial task in Artificial Intelligence (AI) research, particularly with the growing concerns about public security and privacy protection. The authors discuss the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes, offering an exhaustive tutorial for novices. The paper reviews recent advances, typical solutions, and aggregates available research resources. Additionally, it showcases the latest NSVAD research in industrial IoT and smart cities, featuring an end-cloud collaborative architecture for deployable NSVAD. The authors also project future development trends and discuss how AI integration can address existing challenges and promote open opportunities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to detect unusual things happening in videos taken by surveillance cameras in smart cities. As more people use video apps online, it’s becoming important to keep people safe and private. The authors explain how deep learning and computer chips are helping with this task, which they call Video Anomaly Detection (VAD). They also share some new ideas for using VAD in industrial settings like factories and cities. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning
