Benchmarking deep classifiers on mobile devices for vision-based transportation recognition

Richoz, Sebastien (University of Sussex, Brighton, United Kingdom) ; Perez-Uribe, Andres (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Birch, Philip (University of Sussex, Brighton, United Kingdom) ; Roggen, Daniel (University of Sussex, Brighton, United Kingdom)

Vision-based human activity recognition can provide rich contextual information but has traditionally been computationally prohibitive. We present a characterisation of five convolutional neural networks (DenseNet169, MobileNet, ResNet50, VGG16, VGG19) implemented with TensorFlow Lite running on three state of the art Android mobile phones. The networks have been trained to recognise 8 modes of transportation from camera images using the SHL Locomotion and Transportation dataset. We analyse the effect of thread count and back-ends services (CPU, GPU, Android Neural Network API) to classify the images provided by the rear camera of the phones. We report processing time and classification accuracy.


Type de conférence:
full paper
Faculté:
Ingénierie et Architecture
Ecole:
HEIG-VD
Institut:
IICT - Institut des Technologies de l'Information et de la Communication
Adresse bibliogr.:
London, United Kingdom, 9-13 September 2019
Date:
2019-09
London, United Kingdom
9-13 September 2019
Pagination:
5 p.
Veröffentlicht in:
UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 9-13 September 2019, London, United Kingdom
Numérotation (vol. no.):
pp. 803-807
DOI:
ISBN:
9781450368698
Le document apparaît dans:



 Datensatz erzeugt am 2020-05-19, letzte Änderung am 2020-10-27

Fulltext:
Volltext herunterladen
PDF

Dieses Dokument bewerten:

Rate this document:
1
2
3
 
(Bisher nicht rezensiert)