Data Fusion for Deep Learning on Transport Mode Detection: A Case Study
arXiv:2106.05876 · doi:10.1007/978-3-030-80568-5_12
Abstract
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.
12 pages, 2 figures, 4 tables Code avaible at https://github.com/HuguesMoreau/TMD_fusion_benchmark