Abstract:
Our paper aims to build a classification-model which
delineates the typical motion-related activities performed at a
metro station using smart phone sensors. We focus on typical
movements, such as climbing the stairs or moving in the lift,
waiting at security, waiting at the turnstile to check out and,
moving on platform while waiting for a train. Such a classifier
estimates crowd levels in a metro-station (and metro trains in
an indirect sense), thereby adding towards the vision of efficient
metro travel. However, building such a classification-model is
challenging due to non-trivial decision boundaries among the
classes of interest. Our experiments revealed that the best accuracy that a traditional multi-class classifier could obtain was 0.58,
for a four-class classifier. To this end, we proposed a hierarchical
approach of classification which divides the multi-class problem
at hand into a set of manageable two-class classification problems.
These two-class classifiers are then put together, in a hierarchy,
to give an end-to-end solution which takes sensors values from
the phone and predicts the class of motion being observed. Our
model obtained an accuracy of around 0.75, a significantly higher
value, on the real-data collected at Delhi Metro stations. The
same classifiers can potentially be applied to detect crowd levels
at train stations and bus depots, which will make transportation
efficient in smart cities.
