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• W2594865363 abstract "Fall detection plays a significant role in the medical care of elderly people. As reported, falling is the main cause leading to the death of the elderly over the age of 65. Recently, video-based fall detection methods have been widely used, with the progress of intelligent video analysis. In this study, we presents a novel method to detect the fall with a position-fixed RGB camera. Our method consists of four stages. First, we build the background model and segment the foreground, i.e. the moving walker, from the background. The initial state of the pedestrian is supposed to be upright. Under this assumption, we build the human upper-body data base. Second, we extract the contour of the body to obtain the width and height of human body. Initial judgment of a possible fall is made according to the changes of the width and height. Third, we estimate the speed of the human body using an optical flow method. Further judgment of a possible fall is made based on the magnitude and direction of speed. Finally, the exact position of human body is detected by matching image patches with the human upper-body data base. The gesture of the human body, i.e. horizontal or upright, is used to confirm whether a fall has happened or not. The results of the experiment shows the higher accuracy and lower detection error rate of our method." @default.
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• W2594865363 date "2016-12-01" @default.
• W2594865363 modified "2023-09-24" @default.
• W2594865363 title "A novel multi-cue integration system for efficient human fall detection" @default.
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• W2594865363 doi "https://doi.org/10.1109/robio.2016.7866509" @default.
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