Movement Tube Detection Network Integrating 3D CNN and Object Detection Framework to Detect Fall

Abstract

Unlike most of the existing neural network-based fall detection methods, which only detect fall at the time range, the algorithm proposed in this paper detect fall in both spatial and temporal dimension. A movement tube detection network integrating 3D CNN and object detection framework such as SSD is proposed to detect human fall with constrained movement tubes. The constrained movement tube, which encapsulates the person with a sequence of bounding boxes, has the merits of encapsulating the person closely and avoiding peripheral interference. A 3D convolutional neural network is used to encode the motion and appearance features of a video clip, which are fed into the tube anchors generation layer, softmax classification, and movement tube regression layer. The movement tube regression layer fine tunes the tube anchors to the constrained movement tubes. A large-scale spatio-temporal (LSST) fall dataset is constructed using self-collected data to evaluate the fall detection in both spatial and temporal dimensions. LSST has three characteristics of large scale, annotation, and posture and viewpoint diversities. Furthermore, the comparative experiments on a public dataset demonstrate that the proposed algorithm achieved sensitivity, specificity an accuracy of 100%, 97.04%, and 97.23%, respectively, outperforms the existing methods.