Monocular 3-D Gait Tracking in Surveillance Scenes
Gait recognition can potentially provide a noninvasive
and effective biometric authentication from a distance.
However, the performance of gait recognition systems will suffer
in real surveillance scenarios with multiple interacting individuals
and where the camera is usually placed at a significant angle
and distance from the floor. We present a methodology for
view-invariant monocular 3D human pose tracking in man-made
environments in which we assume that observed people move
on a known ground plane. First, we model 3D body poses
and camera viewpoints with a low dimensional manifold and
learn a generative model of the silhouette from this manifold
to a reduced set of training views. During the online stage,
3D body poses are tracked using recursive Bayesian sampling
conducted jointly over the scene's ground plane and the pose-
viewpoint manifold. For each sample, the homography that
relates the corresponding training plane to the image points
is calculated using the dominant 3D directions of the scene,
the sampled location on the ground plane and the sampled
camera view. Each regressed silhouette shape is projected using
this homographic transformation and matched in the image to
estimate its likelihood. Our framework is able to track 3D human
walking poses in a 3D environment exploring only a 4 dimensional
state space with success. In our experimental evaluation, we
demonstrate the significant improvements of the homographic
alignment over a commonly used similarity transformation and
provide quantitative pose tracking results for the monocular
sequences with high perspective effect from the CAVIAR dataset.
Download: pdf
Text Reference
Grégory Rogez, Jonathan Rihan, J.J. Guerrero, and Carlos Orrite. Monocular 3-d gait tracking in surveillance scenes. Cybernetics, IEEE Transactions on, 2013. doi:10.1109/TCYB.2013.2275731.BibTeX Reference
@article{RogezRGO_Cybernetics_2013,author = "Rogez, Gr{\'e}gory and Rihan, Jonathan and Guerrero, J.J. and Orrite, Carlos",
title = "Monocular 3-D Gait Tracking in Surveillance Scenes",
journal = "Cybernetics, IEEE Transactions on",
volume = "PP",
number = "99",
year = "2013",
doi = "10.1109/TCYB.2013.2275731",
publisher = "IEEE",
tag = "people"
}