A vision-based fall detection algorithm of human in indoor environment
Hao Liu, Yongcai Guo · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Elderly care becomes more and more prominent in China as the population is aging fast and the number of aging population is large. Falls, as one of the biggest challenges in elderly guardianship system, have a serious impact on both physical health and mental health of the aged. Based on feature descriptors, such as aspect ratio of human silhouette, velocity of mass center, moving distance of head and angle of the ultimate posture, a novel vision-based fall detection method was proposed in this paper. A fast median method of background modeling with three frames was also suggested. Compared with the conventional bounding box and ellipse method, the novel fall detection technique is not only applicable for recognizing the fall behaviors end of lying down but also suitable for detecting the fall behaviors end of kneeling down and sitting down. In addition, numerous experiment results showed that the method had a good performance in recognition accuracy on the premise of not adding the cost of time.