Improvement of fall detection using consecutive-frame voting

摘要

The Centers for Disease Control and Prevention (CDC) reported the older adult statistics that in every second there is an older adult fall down, 25% of elderly reported a fall in 2014, and it is the first cause of hip fracture in the USA. A fall accident detection system, which can automatically detect the fall accident and call for help, is essential for elderly. This paper proposes Improvement of Fall Detection Using Consecutive-frame Voting. The first step is human detection we propose background subtraction using a mixture of Gaussian models (MoG) combined with average filter model to implement the subtraction results. In feature extraction section, the orientation, aspect ratio and area ratio are calculated from the Principal Component Analysis (PCA) of a human silhouette. The moving object can be classified from the human centroid distance in human centroid tracking section. Each posture will be classified in event classification. Finally, majority voting of the results from consecutive is finally performed. The experimental results show improvement of the accuracy of the proposed method with our previous work which tested on the Le2i dataset.