  Reliable detection of human occupancy in indoor environments is critical for
various energy efficiency, security, and safety applications. We consider this
challenge of occupancy detection using extremely low-quality,
privacy-preserving images from low power image sensors. We propose a combined
few shot learning and clustering algorithm to address this challenge that has
very low commissioning and maintenance cost. While the few shot learning
concept enables us to commission our system with a few labeled examples, the
clustering step serves the purpose of online adaptation to changing imaging
environment over time. Apart from validating and comparing our algorithm on
benchmark datasets, we also demonstrate performance of our algorithm on
streaming images collected from real homes using our novel battery free camera
hardware.
