SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Outdoor distributed computing with split smart messages
Proceedings of the 12th Monterey conference on Reliable systems on unreliable networked platforms
Lightweight object localization with a single camera in wireless multimedia sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Towards cooperative localization of wearable sensors using accelerometers and cameras
INFOCOM'10 Proceedings of the 29th conference on Information communications
Did you see Bob?: human localization using mobile phones
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Seeker-assisted human navigation using smart phones
Proceedings of 1st international symposium on From digital footprints to social and community intelligence
LocateMe: Magnetic-fields-based indoor localization using smartphones
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Probabilistic Inference of Multi-Object Trajectories
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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There has been a shift in the focus of indoor localization research from improving accuracy to minimizing infrastructure requirements [4, 6, 1]. The reason is well understood: since location information only serves as a parameter to location-based services, the cost of deploying localization systems should be a minute fraction of the total cost of provisioning location-based services. We demonstrate the possibility of determining user's location indoors based on what the cameraphone "sees". The camera-phone is worn by the user as a pendant(Figure 1) and images are periodically captured and transmitted over GPRS to a web server. The web server has a database of images with their corresponding location. Upon receiving an image, the web server compares it with stored images, and based on the match, estimates user's location.