Masullo, A;
Burghardt, T;
Damen, D;
Hannuna, S;
Ponce-Lopez, V;
Mirmehdi, M;
(2019)
Calorinet: From silhouettes to calorie estimation in private environments.
British Machine Vision Conference 2018, BMVC 2018
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Abstract
We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated based on a publicly available dataset, SPHERE-Calorie, linking RGB-D, inertial and calorific measurements. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.
| Type: | Article |
|---|---|
| Title: | Calorinet: From silhouettes to calorie estimation in private environments |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://bmvc2018.org/ |
| Language: | English |
| Additional information: | © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10111762 |
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