Few Shot Clustering For Indoor Occupancy Detection With Extremely Low Quality Images From Battery Free Cameras
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 environments 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 that also leads to a new dataset for the vision community.
Committee: Jin Tian (major professor), Soumik Sarkar (major professor), and Ali Jannesari
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