Poster: User Location Fingerprinting at Scale

Many emerging mobile computing applications are continuous vision based. The primary challenge these applications face is computation partitioning between the phone and cloud. The indoor location information is one metadata that can help these applications in making this decision. In this extended-abstract, we propose a vision based scheme to uniquely fingerprint an environment which can in turn be used to identify user's location from the uploaded visual features. Our approach takes into account that the opportunity to identify location is fleeting and the phones are resource constrained -- therefore minimal yet sufficient computation needs to be performed to make the offloading decision. Our work aims to achieve near real-time performance while scaling to buildings of arbitrary sizes. The current work is in preliminary stages but holds promise for the future -- may apply to many applications in this area.