The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
The n-hop multilateration primitive for node localization problems
Mobile Networks and Applications
Indoor Localization Using Camera Phones
WMCSA '06 Proceedings of the Seventh IEEE Workshop on Mobile Computing Systems & Applications
Building a sensor network of mobile phones
Proceedings of the 6th international conference on Information processing in sensor networks
Tracking mobile nodes using RF Doppler shifts
Proceedings of the 5th international conference on Embedded networked sensor systems
Lifelogging memory appliance for people with episodic memory impairment
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Using wearable sensors and real time inference to understand human recall of routine activities
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Enhanced shopping: a dynamic map in a retail store
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
AAMPL: accelerometer augmented mobile phone localization
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Augmenting mobile localization with activities and common sense knowledge
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Trajectory mining from anonymous binary motion sensors in Smart Environment
Knowledge-Based Systems
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This work describes a new approach for localizing people by cooperative sensor fusion of lightweight camera and wearable accelerometer measurements. We present the algorithm to identify people moving around as they are detected by cameras deployed in the infrastructure. The algorithm uses a correlation metric to develop an ID matching algorithm that can associate people in the scene to their global ID emitted from a wireless accelerometer sensor node worn on their belts. First we conduct a set of preliminary experiments to verify that the quantities of interest easily measurable by off-the-shelf components. We validate our metric and the performance of the proposed ID matching algorithm using simulations on testbed data that also includes a crowded scenario.