Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Bayesian Multiple Target Tracking
Bayesian Multiple Target Tracking
CASA '03 Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA 2003)
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Human Tracking using Floor Sensors based on the Markov Chain Monte Carlo Method
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A wireless LAN-based indoor positioning technology
IBM Journal of Research and Development
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Pedestrian Tracking with Shoe-Mounted Inertial Sensors
IEEE Computer Graphics and Applications
Combining accelerometer and video camera: reconstruction of bow velocity profiles
NIME '06 Proceedings of the 2006 conference on New interfaces for musical expression
Proceedings of the 4th international conference on Embedded networked sensor systems
Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation
International Journal of Robotics Research
Tracking mobile nodes using RF Doppler shifts
Proceedings of the 5th international conference on Embedded networked sensor systems
Identifying people in camera networks using wearable accelerometers
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
People tracking with anonymous and ID-sensors using Rao-Blackwellised particle filters
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Radio Tomographic Imaging with Wireless Networks
IEEE Transactions on Mobile Computing
Multi-feature graph-based object tracking
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
A survey on multi person identification and localization
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Smartphone-based pedestrian tracking in indoor corridor environments
Personal and Ubiquitous Computing
I see you there!: developing identity-preserving embodied interaction for museum exhibits
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Moving Beyond Weak Identifiers for Proxemic Interaction
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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We present a method to identify and localize people by leveraging existing CCTV camera infrastructure along with inertial sensors (accelerometer and magnetometer) within each person's mobile phones. Since a person's motion path, as observed by the camera, must match the local motion measurements from their phone, we are able to uniquely identify people with the phones' IDs by detecting the statistical dependence between the phone and camera measurements. For this, we express the problem as consisting of a two-measurement HMM for each person, with one camera measurement and one phone measurement. Then we use a maximum a posteriori formulation to find the most likely ID assignments. Through sensor fusion, our method largely bypasses the motion correspondence problem from computer vision and is able to track people across large spatial or temporal gaps in sensing. We evaluate the system through simulations and experiments in a real camera network testbed.