Comparative Study Of Computer Vision Algorithms For Fall Detection

Objective: To compare the performance of three human fall recognition algorithms, focusing on computer vision. The comparison will be carried out by evaluating their performance on various databases commonly used by the scientific community, as well as on a new database called CAUCAFall. Methodology: The study compares three algorithms, selected through a systematic review that considered articles working with computer vision, RGB cameras and public databases. The selected algorithms focus on feature extraction and convolutional neural networks using YOLO and OPENPOSE. Results: The study found that all three algorithms perform well on databases commonly used by the scientific community. However, inferior performance was observed when evaluating the algorithms on CAUCAFall, which contains loosely controlled environments closer to reality. Conclusions: The research highlights the importance of evaluating human fall recognition algorithms in more realistic scenarios. It raises the importance of future research that focuses on creating and evaluating algorithms on databases that contain scenarios closer to reality, which would be a significant advance in the area of human fall recognition.