Degree Type

Thesis

Date of Award

2021

Degree Name

Master of Science

Department

Computer Science

Major

Computer Science

First Advisor

Soumik Soumik Sarkar

Abstract

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 that also leads to a new dataset for the vision community

DOI

https://doi.org/10.31274/etd-20210609-164

Copyright Owner

Homagni Saha

Language

en

File Format

application/pdf

File Size

38 pages

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