Summary of Vars: Vision-based Assessment Of Risk in Security Systems, by Pranav Gupta et al.
VARS: Vision-based Assessment of Risk in Security Systems
by Pranav Gupta, Pratham Gohil, Sridhar S
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Machine learning models are being tested for their ability to accurately predict danger levels in video content, which is crucial for safety and security systems. A custom dataset of 100 videos with 50 frames each was used, with human-rated danger scores ranging from 0 to 10. The danger ratings were classified into three categories: no alert (less than 7) and high alert (greater than or equal to 7). Classical machine learning models like Support Vector Machines, Neural Networks, and transformer-based models were evaluated using accuracy, F1-score, and mean absolute error (MAE) metrics. The results showed that certain models performed better than others in predicting danger levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about using computers to predict how dangerous a video is. It’s important for making sure people are safe and secure. To test this, the researchers used 100 videos with 50 frames each, and had people rate how dangerous they were from 0 to 10. The ratings were grouped into two categories: no alert (less than 7) and high alert (greater than or equal to 7). They tested different computer models to see which one worked best. This will help make a better system for checking videos. |
Keywords
» Artificial intelligence » F1 score » Machine learning » Mae » Transformer
