Data Fusion for Deep Learning on Transport Mode Detection: A Case Study - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année :

Data Fusion for Deep Learning on Transport Mode Detection: A Case Study

(1, 2) , (1) , (3, 2)
1
2
3

Résumé

In Transport Mode Detection, a great diversity of methodologies exist according to the choice made on sensors, preprocessing, model used, etc. In this domain, the comparisons between each option are not always complete. Experiments on a public, real-life dataset are led here to evaluate carefully each of the choices that were made, with a specific emphasis on data fusion methods. Our most surprising finding is that none of the methods we implemented from the literature is better than a simple late fusion. Two important decisions are the choice of a sensor and the choice of a representation for the data: we found that using 2D convolutions on spectrograms with a logarithmic axis for the frequencies was better than 1-dimensional temporal representations. To foster the research on deep learning with embedded inertial sensors, we release our code along with our publication.
Fichier principal
Vignette du fichier
Data_Fusion_ Deep_Learning _EANN_camera_ready.pdf (453.96 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03242601 , version 1 (31-05-2021)
hal-03242601 , version 2 (06-07-2021)
hal-03242601 , version 3 (30-09-2021)

Identifiants

Citer

Hugues Moreau, Andrea Vassilev, Liming Chen. Data Fusion for Deep Learning on Transport Mode Detection: A Case Study. 22nd Conference on Engineering Applications of Neural Networks, Sep 2021, Online, Greece. ⟨10.1007/978-3-030-80568-5_12⟩. ⟨hal-03242601v3⟩
89 Consultations
158 Téléchargements

Altmetric

Partager

Gmail Facebook Twitter LinkedIn More