Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
465588 | Pervasive and Mobile Computing | 2015 | 22 Pages |
Abstract
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.
Keywords
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Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Tanusri Bhattacharya, Lars Kulik, James Bailey,