Human fall detection using normalized shape aspect ratio

In video surveillance, automatic human fall detection is important to protect vulnerable groups such as the elderly. When the camera layout varies, the shape aspect ratio (SAR) of a human body may change substantially. In order to rectify these changes, in this paper, we propose an automatic human fall detection method using the normalized shape aspect ratio (NSAR). A calibration process and bicubic interpolation are implemented to generate the NSAR table for each camera. Compared with some representative fall detection methods using the SAR, the proposed method integrates the NSAR with the moving speed and direction information to robustly detect human fall, as well as being able to detect falls toward eight different directions for multiple humans. Moreover, while most of the existing fall detection methods were designed only for indoor environment, experimental results demonstrate that this newly proposed method can effectively detect human fall in both indoor and outdoor environments.