Vision based human fall detection with Siamese convolutional neural networks

Fall detection is drawing serious attention all across the globe, as unattended fall of senior citizens creates long lasting injuries. This necessitates the deployment of automatic fall detection systems to facilitate smart care health environments for the elderly people living in various settings, viz., living independently in their homes, hospitalized or living in care homes. The proposed work employs Siamese network with one shot classification for human fall detection. Unlike the neural network that classifies the video sequences, this network learns to differentiate the video sequences by computing the similarity score. The network contains two identical CNNs, receiving pair of video sequences as the input. The features of these networks are merged at the final layer through the similarity function. Two different architectures viz., one with 2D convolutional filters and the other with depth wise convolutional filters, each operated on two set of features, RGB and optical flow features are developed. Experimental results demonstrate the effectiveness and feasibility of the proposed work compared to state-of-the methods.