Abstract
Currently, falling is the primary cause of injury and death of the elderly due to accidents, which seriously threatens the health and life of the elderly. Considering the phenomenon of video privacy disclosure in recent years, this article proposes a computer vision fall detection method to meet the needs of video privacy protection. The innovation here lies in compressed sensing (CS) visual privacy protection and GAN-based feature enhancement. There are three main steps, the video frame visual privacy protection with chaotic pseudo-random CS mechanism, the foreground extraction with improved low rank sparse decomposition theory, and the temporal and spatial feature enhancement and fall detection with improved-ACGAN 'architecture. The experimental results on three open fall datasets show that the method can not only effectively detect the fall behavior in video, but also has high accuracy. At the same time, the overall operation speed of the algorithm increases with the increase of the number of compression layers.
| Original language | English |
|---|---|
| Pages (from-to) | 14-23 |
| Number of pages | 10 |
| Journal | IEEE Multimedia |
| Volume | 29 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 1 Oct 2022 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 1994-2012 IEEE.
Funding
This work was supported in part by the Provincial Natural Science Foundation of the Science and Technology Bureau of Jiangsu Province under Grant BK20180088, in part by the China Postdoctoral Science Foundation under Grant 2019M651916, in part by the Postdoctoral Research Project of Zhejiang Province under Grant zj2019025, and in part by the Natural Science Foundation of China under Grant 61871445
Keywords
- Chaotic pseudorandom mechanism
- Computer vision
- Fall detection
- Feature enhancement
- Feature extraction
- Foreground extraction
- Older adults
- Privacy
- Sparse matrices
- Video fall detection
- Visual privacy protection
- Visualization