Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras
This work addresses the problem of human occupancy detection for energy efficiency, security, and safety applications, using a novel approach that is incremental in combining existing techniques for a specific domain.
The paper tackled occupancy detection in indoor environments using extremely low-quality images from battery-free cameras, proposing a combined few-shot learning and clustering algorithm that achieved reliable performance with low commissioning costs, as validated on benchmark datasets and real-world data from homes.
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.