Detection of human presence is a key feature in Human Computer Interaction. Solutions based on cameras are attractive, but require computer vision techniques to extract meaningful data, which can be expensive from a computational point of view. In this work, we present a new system that merges a low resolution thermal camera with advanced feature extraction techniques such as Convolutional Neural Networks. We demonstrate the possibility to adapt their execution to resource-constrained platform without significant loss of performance, by processing data on a 32-bit low power microcontroller, performing the classification on thermal video stream. It achieve 76.7% of accuracy in the microcontroller, requiring only 16.5 mW in continuous classification mode and using 6 kB of RAM.

Convolutional Neural Network on Embedded Platform for People Presence Detection in Low Resolution Thermal Images

Cerutti, Gianmarco;Farella, Elisabetta
2019

Abstract

Detection of human presence is a key feature in Human Computer Interaction. Solutions based on cameras are attractive, but require computer vision techniques to extract meaningful data, which can be expensive from a computational point of view. In this work, we present a new system that merges a low resolution thermal camera with advanced feature extraction techniques such as Convolutional Neural Networks. We demonstrate the possibility to adapt their execution to resource-constrained platform without significant loss of performance, by processing data on a 32-bit low power microcontroller, performing the classification on thermal video stream. It achieve 76.7% of accuracy in the microcontroller, requiring only 16.5 mW in continuous classification mode and using 6 kB of RAM.
978-1-4799-8131-1
978-1-4799-8132-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/319106
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