Summary of Faker: Full-body Anonymization with Human Keypoint Extraction For Real-time Video Deidentification, by Byunghyun Ban and Hyoseok Lee
FAKER: Full-body Anonymization with Human Keypoint Extraction for Real-time Video Deidentification
by Byunghyun Ban, Hyoseok Lee
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 proposed novel approach employs a smaller GAN model for real-time full-body anonymization of individuals in videos, achieving effective removal of personal identification information such as skin color, clothing, accessories, and body shape. Unlike traditional methods, this method successfully erases all details while leveraging pose estimation algorithms to accurately represent individuals’ positions, movements, and postures. The algorithm can be seamlessly integrated into CCTV or IP camera systems for widespread adoption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study proposes a new way to protect people’s privacy in videos by making their bodies invisible. It uses a special computer model that can erase personal details like skin color, clothes, and accessories while keeping the person’s movements and posture intact. This technology could be used in cameras installed in factories, stores, or other places where privacy is important. |
Keywords
* Artificial intelligence * Gan * Pose estimation
