Abstract
We propose a novel method for automatic detection of the transport mode of a person carrying a Smartphone. Existing approaches assume idealized positioning data with no GPS signal losses, require informa
tion from additional external sources such as real time bus locations, or only allow for a coarse distinction between very few categories (e.g. ´still´, ´walk´, ´motorized´). Our approach is designed to deal with cluttered real-world Smartphone data and can distinguish between fine-grained transport mode categories. It is robust against GPS signal losses by including positioning data obtained from the cellular network and data from accelerometer readings. Mode detection is performed by a two-stage classification technique using randomized ensemble of classifiers combined with a Hidden Markov Model. We report promising results of an experimental performance analysis with real-world data collected by 15 volunteers during their everyday routines over a period of two months.
Originalsprache | Englisch |
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Titel | Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) |
Seitenumfang | 4 |
Publikationsstatus | Veröffentlicht - 2012 |
Veranstaltung | 21st International Conference on Pattern Recognition (ICPR2012) - Dauer: 11 Nov. 2012 → 15 Nov. 2012 |
Konferenz
Konferenz | 21st International Conference on Pattern Recognition (ICPR2012) |
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Zeitraum | 11/11/12 → 15/11/12 |
Research Field
- Ehemaliges Research Field - Mobility Systems