Crowdsourcing Based Mobile Location Recognition with Richer Fingerprints from Smartphone Sensors

With the rapid advancements of mobile computing, mobile location recognition is becoming an important and useful service, which recognizes the logical locations of places/scenes that users are interested in, instead of physical coordinates. Most of the existing mobile location recognition systems utilize the image as visual fingerprint of a place, and need to construct a large-scale visual fingerprint database in advance. However, collecting visual fingerprints is a labor-intensive and time-consuming procedure. In order to address this problem, we propose a novel crowdsourcing-based framework, and leverage a variety of sensors embedded in smartphones to collect richer location fingerprints for exploring their positive effects. To achieve higher recognition accuracy, we propose an object-centric fingerprint searching which can sufficiently take advantage of smartphone sensors and determine more accurate searching space than the traditional user-centric method. We build a crowdsourcing-based database with richer fingerprints and implement a location recognition system, called CrowdLR. Extensive experiments verify that our object-centric method can achieve promising results maintaining around 10% precision higher than the user-centric method.