Tracking People in 3D Using a Bottom-Up Top-Down Detector
L.Spinello, M.Luber, K.O.Arras
Int. Conf. Robotics and Automation 2011 (ICRA)
Keywords: 3D people detection and tracking, range data processing
People detection and tracking is a key component
for robots and autonomous vehicles in human environments.
While prior work mainly employed image or 2D range data for
this task, in this paper, we address the problem using 3D range
data. In our approach, a top-down classifier selects hypotheses
from a bottom-up detector, both based on sets of boosted
features. The bottom-up detector learns a layered person model
from a bank of specialized classifiers for different height levels
of people that collectively vote into a continuous space. Modes
in this space represent detection candidates that each postulate
a segmentation hypothesis of the data. In the top-down step,
the candidates are classified using features that are computed
in voxels of a boosted volume tessellation. We learn the optimal
volume tessellation as it enables the method to stably deal with
sparsely sampled and articulated objects. We then combine the
detector with tracking in 3D for which we take a multi-target
multi-hypothesis tracking approach. The method neither needs
a ground plane assumption nor relies on background learning.
The results from experiments in populated urban environments demonstrate 3D tracking and highly robust people
detection up to 20 m with equal error rates of at least 93%.