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.
|Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)
|Veröffentlicht - 2012
|21st International Conference on Pattern Recognition (ICPR2012) -
Dauer: 11 Nov. 2012 → 15 Nov. 2012
|21st International Conference on Pattern Recognition (ICPR2012)
|11/11/12 → 15/11/12
- Ehemaliges Research Field - Mobility Systems