Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging

Automatic fall detection is a vital technology for ensuring health and safety of people. Home based camera systems for fall detection often put people's privacy at risk. Thermal cameras can partially/fully obfuscate facial features, thus preserving the privacy of a person. Another challenge is the less occurrence of falls in comparison to 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 camera. We present a novel adversarial network that comprise of two channel 3D convolutional auto encoders; one each handling video sequences and optical flow, which then reconstruct the thermal data and the optical flow input sequences. We introduce a differential constraint, a technique to track the region of interest and a joint discriminator to compute the reconstruction error. Larger reconstruction error indicates the occurrence of fall in a video sequence. The experiments on a publicly available thermal fall dataset show the superior results obtained in comparison to standard baseline.