Automatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersections

Stay points are important for recognizing significant places from a mobile user's GPS trajectory. Such places are often located indoors and in urban canyons, where GPS is unreliable. Consequently, mapping a user's stay point to a Place of Interest (POI) using only GPS data is particularly challenging. Our novel algorithm employs both spatio-temporal density estimation and line count inference to predict and rank a user's POI(s) at building level accuracy from noisy time-annotated GPS data points. An experimental study demonstrates the superiority of our algorithm against several baseline approaches with a recall of 96.5% for the top 5 retrieved locations.