Privacy-preserving video fall detection via chaotic compressed sensing and GAN-based feature enhancement

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 paper 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: 1) the video frame visual privacy protection with chaotic pseudo-random CS mechanism, 2) the foreground extraction with improved low rank sparse decomposition theory, 3) 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.