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

• URL: http://arxiv.org/abs/2004.08352v2
• Date: Sat, 24 Oct 2020 22:06:49 GMT
• Title: Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging
• Authors: Vineet Mehta, Abhinav Dhall, Sujata Pal, Shehroz S. Khan
• Abstract summary: Home-based camera systems for fall detection often put people's privacy at risk. As fall occurs rarely, it is non-trivial to learn algorithms due to class imbalance. We formulate fall detection as an anomaly detection within an adversarial framework using thermal imaging.
• Score: 8.110295985047278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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