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PERCOM
2015
ACM

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

4 years 7 months ago
Automatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersec
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.
Tanusri Bhattacharya, Lars Kulik, James Bailey
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where PERCOM
Authors Tanusri Bhattacharya, Lars Kulik, James Bailey
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