title: Model-based reinforcement of Kinect depth data for human motion capture applications.
abstract: Motion capture systems have recently experienced a strong evolution. New cheap depth sensors and open source frameworks, such as OpenNI, allow for perceiving human motion on-line without using invasive systems. However, these proposals do not evaluate the validity of the obtained poses. This paper addresses this issue using a model-based pose generator to complement the OpenNI human tracker. The proposed system enforces kinematics constraints, eliminates odd poses and filters sensor noise, while learning the real dimensions of the performer's body. The system is composed by a PrimeSense sensor, an OpenNI tracker and a kinematics-based filter and has been extensively tested. Experiments show that the proposed system improves pure OpenNI results at a very low computational cost.
authors.affiliation: Polythecnic School of Cáceres, University of Extremadura, Cáceres 10003, Spain. lvcalderita@unex.es
authors.forename: Luis Vicente
authors.initials: LV
authors.lastname: Calderita
authors.forename: Juan Pedro
authors.initials: JP
authors.lastname: Bandera
authors.forename: Pablo
authors.initials: P
authors.lastname: Bustos
authors.forename: Andreas
authors.initials: A
authors.lastname: Skiadopoulos
mesh_terms: D000054:Acceleration; D056044:Actigraphy / Q000295:instrumentation*; D001699:Biometry / Q000295:instrumentation*; D003198:Computer Simulation; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005868:Gestures; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation*; D008954:Models, Biological; D014159:Transducers; D018910:Video Games