Paper Info
Title
Probabilistic Tracking of Pedestrian Movements via In-Floor Force Sensing
Abstract
This article presents a probabilistic approach to the tracking and estimation of the lower body posture of users moving on foot over an instrumented floor surface. The latter consists of an array of low-cost force platforms providing intermittent foot-floor contact data with limited spatial resolution. We use this data to track body posture in 3D space using Bayesian filters with a switching state-space model. Potential applications of this work to person tracking and human-computer interaction are described.
Year
DOI
Venue
2010
10.1109/CRV.2010.26
CRV
Keywords
Field
DocType
probabilistic tracking,in-floor sensing,bayesian filter,body posture tracking,kinematic tracking,human computer interaction,intermittent foot-floor contact data,potential application,foot-floor contact data,pedestrian movements,pedestrian movement tracking,image resolution,body posture,person tracking,lower body posture,switching state-space model,spatial resolution,particle filter,motion estimation,instrumented floor surface,low-cost force platform,filtering theory,bayesian filters,limited spatial resolution,in-floor force sensing,probability,human-computer interaction,state space model,dynamics,force,foot,tracking
Computer vision,Pedestrian,Computer science,Particle filter,Force platform,Tracking system,Artificial intelligence,Motion estimation,Probabilistic logic,Image resolution,Bayesian probability
Conference
ISBN
Citations 
PageRank 
978-1-4244-6963-5
3
0.42
References 
Authors
16
3
Name
Order
Citations
PageRank
Rishi Rajalingham1131.58
Yon Visell215422.97
Jeremy R. Cooperstock3449102.09