Browsing by Author "Gafurova, Lina"

Automatic fall detection is a very important challenge in the public health care domain. The problem primarily concerns the growing population of the elderly, who are at considerably higher risk of falling down. Moreover, the fall downs for the elderly may result in serious injuries or even death. In this work we propose a solution for fall detection based on machine learning, which can be integrated into a monitoring system as a detector of fall downs in image sequences. Our approach is solely camera-based and is intended for indoor environments. For successful detection of fall downs, we utilize the combination of the human shape variation determined with the help of the approximated ellipse and the motion history. The feature vectors that we build are computed for sliding time windows of the input images and are fed to a Support Vector Machine for accurate classification. The decision for the whole set of images is based on additional rules, which help us restrict the sensitivity of the method. To fairly evaluate our fall detector, we conducted extensive experiments on a wide range of normal activities, which we used to oppose the fall downs. Reliable recognition rates suggest the effectiveness of our algorithm and motivate us for improvement.