Human and machine recognition of transportation modes from body-worn camera images

Richoz, Sebastian, Ciliberto, Mathias, Wang, Lin, Birch, Philip, Gjoreski, Hristijan and Perez-Uribe, Andres (2019) Human and machine recognition of transportation modes from body-worn camera images. International Conference on Activity and Behavior Computing, Spokane, Eastern Washington University, USA, May. 30 - Jun. 2, 2019. Published in: Inoue, Sozo and Rahman Ahad, Atiqur, (eds.) International Conference on Activity and Behavior Computing. Institute of Electrical and Electronics Engineers (Accepted)

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Abstract

Computer vision techniques applied on images opportunistically captured from body-worn cameras or mobile phones offer tremendous potential for vision-based context awareness. In this paper, we evaluate the potential to recognise the modes of locomotion and transportation of mobile users, by analysing single images captured by body-worn cameras. We evaluate this with the publicly available Sussex-Huawei Locomotion and Transportation Dataset, which includes 8 transportation and locomotion modes performed over 7 months by 3 users. We present a baseline performance obtained through crowd sourcing using Amazon Mechanical Turk. Humans infered the correct modes of transportations from images with an F1-score of 52%. The performance obtained by five state-of-the-art Deep Neural Networks (VGG16, VGG19, ResNet50, MobileNet and DenseNet169) on the same task was always above 71.3% F1-score. We characterise the effect of partitioning the training data to fine-tune different number of blocks of the deep networks and provide recommendations for mobile implementations.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Sensor Technology Research Centre
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
T Technology
Depositing User: Daniel Roggen
Date Deposited: 24 Jun 2019 14:30
Last Modified: 25 Jun 2019 14:59
URI: http://sro.sussex.ac.uk/id/eprint/84474

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