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. |
Year | DOI | Venue |
---|---|---|
2020 | 10.1109/ICPR48806.2021.9412632 | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
Keywords | DocType | ISSN |
Fall detection, adversarial learning, thermal | Conference | 1051-4651 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Vineet Mehta | 1 | 1 | 1.38 |
Abhinav Dhall | 2 | 1035 | 52.61 |
Pal Sujata | 3 | 0 | 0.34 |
Khan Shehroz | 4 | 0 | 0.34 |