Tracking human position and lower body parts using Kalman and particle filters constrained by human biomechanics.
In this paper, a novel framework for visual tracking of human body parts is introduced. The approach presented demonstrates the feasibility of recovering human poses with data from a single uncalibrated camera by using a limb-tracking system based on a 2-D articulated model and a double-tracking strategy. Its key contribution is that the 2-D model is only constrained by biomechanical knowledge about human bipedal motion, instead of relying on constraints that are linked to a specific activity or camera view. These characteristics make our approach suitable for real visual surveillance applications. Experiments on a set of indoor and outdoor sequences demonstrate the effectiveness of our method on tracking human lower body parts. Moreover, a detail comparison with current tracking methods is presented.
Martinez del Rincon
Jesús
J
Digital Imaging Research Centre, Kingston University, KT1 2EE Surrey, UK. Jesus.Martinezdelrincon@kingston.ac.uk
Makris
Dimitrios
D
Orrite Urunuela
Carlos
C
Nebel
Jean-Christophe
JC
D000465:Algorithms; D001696:Biomechanical Phenomena; D003491:Cybernetics; D005684:Gait / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D035002:Lower Extremity / Q000033:anatomy & histology*; D008954:Models, Biological; D011187:Posture / Q000502:physiology*; D014741:Video Recording; D016138:Walking / Q000502:physiology*