Kinematic-based Markerless Human Tracking in 3D Probabilistic Occupancy Grids

  • Rodrigo Andrade de Bem Federal University of Rio Grande
  • Maurício Goulart Federal University of Rio Grande
  • Gisele Simas Federal University of Rio Grande
  • Silvia Botelho Federal University of Rio Grande

Abstract

Markerless human motion tracking can be employed
in many applications such as automatic surveillance, motion
capture, human-machine interface and activity recognition. This
problem has been extensively studied in the computer vision
research community in the last years. In this context, the present
paper presents an approach for 3D markerless human motion
tracking based on a skeletal kinematic model of the human body.
This method is applied over a 3D probabilistic occupancy grid
of the environment, which is constructed by means of a Bayesian
fusion of images from multiple synchronized sensoring cameras.
Although the use of kinematic models in 3D human tracking is
widely employed, its use over 3D probabilistic occupancy grids
still was not vastly investigated in the literature. The experiments
were performed using a public dataset with video sequences of
people in motion. The results show that the method is capable
of dealing adequately with the 3D markerless human motion
tracking problem.

Author Biography

Rodrigo Andrade de Bem, Federal University of Rio Grande
Assistant Professor at Computational Sciences Center (C3) of Federal University of Rio Grande (FURG) and member of Automation and Intelligent Robotics Group (NAUTEC).
Published
21-10-2015
How to Cite
Andrade de Bem, R., Goulart, M., Simas, G., & Botelho, S. (2015). Kinematic-based Markerless Human Tracking in 3D Probabilistic Occupancy Grids. Journal of Communication and Information Systems, 30(1). https://doi.org/10.14209/jcis.2015.12
Section
Regular Papers