Automatic fall detection of human in video using combination of features

The problem of automatically fall detection of older people living alone is a popular research topic since falls are one of the major health hazards among the aging population aged 65 and above and the population of them in China is more than 100 million. In this paper, we present an automatic human fall detection framework based on video surveillance which can improve safety of elders in indoor environments. First, a vision component was used to detect and extract moving people in videos from static cameras. Then, we combine Histograms of Oriented Gradients(HOG),Local Binary Pattern(LBP)and feature extracted by the Deep Learning Framework Caffe to form a new augmented feature and the feature is named HLC. We use HLC to represent a person's motion state in a frame of a video sequence. Because the process of fall is a sequence of movements, we use HLC features which were extracted from continuous frames of a video sequence to implement the fall detection. With the help of the HLC feature, we achieve an average fall detection result of 93.7% sensitivity and 92.0% specificity on three different datasets.