  Automatic fall detection is a vital technology for ensuring the health and
safety of people. Home-based camera systems for fall detection often put
people's privacy at risk. Thermal cameras can partially or fully obfuscate
facial features, thus preserving the privacy of a person. Another challenge is
the less occurrence of falls in comparison to the normal activities of daily
living. As fall occurs rarely, it is non-trivial to learn algorithms due to
class imbalance. To handle these problems, we formulate fall detection as an
anomaly detection within an adversarial framework using thermal imaging. We
present a novel adversarial network that comprises of two-channel 3D
convolutional autoencoders which reconstructs the thermal data and the optical
flow input sequences respectively. We introduce a technique to track the region
of interest, a region-based difference constraint, and a joint discriminator to
compute the reconstruction error. A larger reconstruction error indicates the
occurrence of a fall. The experiments on a publicly available thermal fall
dataset show the superior results obtained compared to the standard baseline.

Computer Vision and Pattern Recognition;Machine Learning