A video surveillance system designed to detect multiple falls - Ming-Chih Chen (Advances in Mechanical Engineering, 2016)
This work presents a fall detection system that is based on image processing technology. The system can detect falling by various humans via analysis of video frame. First, the system utilizes the method of mixture and Gaussian background model to generate information about the background, and the noise and shadow of background are eliminated to extract the possible positions of moving objects. The extraction of a foreground image generates more noise and damage. Therefore, morphological and size filters are utilized to eliminate this noise and repair the damage to the image. Extraction of the foreground image yields the locations of human heads in the image. The median point, height, and aspect ratio of the people in the image are calculated. These characteristics are utilized to trace objects. The change of the characteristics of objects among various consecutive images can be used to evaluate those persons enter or leave the scene. The method of fall detection uses the height and aspect ratio of the human body, analyzes the image in which one person overlaps with another, and detects whether a human has fallen or not. Experimental results demonstrate that the proposed method can efficiently detect falls by multiple persons.