Fall detection using optical level anonymous image sensing system

Fall is one of the leading causes of injury for the elderly individuals. Systems that automatically detect falls can significantly reduce the delay of assistance. Most of commercialized fall detection systems are based on wearable devices, which elderly individuals tend to forget wearing. Using surveillance cameras to detect falls based on computer vision is ideal, because anyone in the monitoring scopes can be under protection. However, the privacy protection issue using surveillance cameras has been bothering people. To effectively protect the privacy, we proposed an optical level anonymous image sensing system, which can protect the privacy by hiding the facial regions optically at the video capturing phase. We apply the system to fall detection. In detecting falls, we propose a neural network by combining a 3D convolutional neural network for feature extraction and an autoencoder for modelling the normal behaviors. The learned autoencoder reconstructs the features extracted from videos with normal behaviors with smaller average errors than those extracted from videos with falls. We evaluated our neural network by a hold-out validation experiment, and showed its effectiveness. In field tests, we showed and discussed the applicability of the optical level anonymous image sensing system for privacy protection and fall detection.