A Deep Residual Network for Recognizing Transportation Vehicles using Smartphone Sensors
Issued Date
2022-01-01
Resource Type
ISSN
23270586
eISSN
23270594
Scopus ID
2-s2.0-85141933588
Journal Title
Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
Volume
2022-October
Start Page
209
End Page
213
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS Vol.2022-October (2022) , 209-213
Suggested Citation
Hnoohom N., Maitrichit N., Mekruksavanich S., Jitpattanakul A. A Deep Residual Network for Recognizing Transportation Vehicles using Smartphone Sensors. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS Vol.2022-October (2022) , 209-213. 213. doi:10.1109/ICSESS54813.2022.9930314 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84336
Title
A Deep Residual Network for Recognizing Transportation Vehicles using Smartphone Sensors
Author's Affiliation
Other Contributor(s)
Abstract
The development of sensor technology has enabled the development of a variety of applications for human activity detection by wearable devices. Identifying transportation modes for contextual support in the execution of systems such as driver assistance and intelligent transportation planning is one of the advantageous applications of an intelligent transportation system (ITS). Due to the widespread use of smartphones in today's world, a mobile application-based solution is proposed that can significantly reduce the cost of implementing ITS. In this work, we recognized transportation vehicles using the accelerometer and gyroscope data collected by smartphones. To achieve the research goal, this work developed a deep residual network called DeepResNeXt that used convolutional kernels and residual connections for transportation vehicle recognition. We used a public benchmark dataset to evaluate the proposed deep residual network. Experimental results showed that DeepResNeXt was achieved better accuracy and F1-score than previous works. In addition, this work also investigated the effect of sensor types on recognition performance. The results showed that the deep residual network trained with accelerometers achieved higher accuracy and F1-score than the network trained with gyroscope data.