CrowdOLR: Toward Object Location Recognition With Crowdsourced Fingerprints Using Smartphones

摘要

Recognizing object location by taking a photo with smartphones is useful for many location-based services. However, start-of-the-art technologies for both localization and location recognition have difficulty in achieving satisfactory performance. Moreover, it is a challenging issue to construct and maintain a large-scale image database for existing visual-based location recognition systems. To cope with these issues, we introduce CrowdOLR, a crowdsourcing based object location recognition system, which collects one location image together with various rich sensory data (GPS coordinates, azimuth angle, tilt angle, etc.) as a fingerprint of a location query and matches it to a fingerprint database crowdsourced from users' smartphones. We designed a simple and efficient user action mode and proposed a series of fingerprint extracting, searching, and matching methods, so that CrowdOLR satisfies five desirable properties: high recognition accuracy, user friendliness, quick response, no/little site survey, and timely update. We implemented CrowdOLR and collected 8100 location fingerprints of 162 objects for performance evaluation. Extensive experiments demonstrate that CrowdOLR achieves promising results in various complicated and realistic scenarios.