Summary of Intelligent Video Recording Optimization Using Activity Detection For Surveillance Systems, by Youssef Elmir et al.
Intelligent Video Recording Optimization using Activity Detection for Surveillance Systems
by Youssef Elmir, Hayet Touati, Ouassila Melizou
First submitted to arxiv on: 4 Nov 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 The paper proposes an optimized video recording solution for surveillance systems, addressing the issue of managing vast amounts of footage. The hybrid method combines motion detection via frame subtraction with object detection using YOLOv9 to target scenes involving human or car activity, reducing unnecessary footage and optimizing storage usage. The model demonstrates superior performance in precision metrics (0.855 for car detection and 0.884 for person detection) and reduces storage requirements by two-thirds compared to traditional surveillance systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps improve surveillance systems’ efficiency by focusing on recording only important activities. It combines two methods, motion detection and object detection, to find relevant scenes. This makes the system use less storage space, which is helpful for managing a lot of video footage. |
Keywords
» Artificial intelligence » Object detection » Precision
