  For the 2D laser-based tasks, e.g., people detection and people tracking, leg
detection is usually the first step. Thus, it carries great weight in
determining the performance of people detection and people tracking. However,
many leg detectors ignore the inevitable noise and the multiscale
characteristics of the laser scan, which makes them sensitive to the unreliable
features of point cloud and further degrades the performance of the leg
detector. In this paper, we propose a multiscale adaptive-switch Random Forest
(MARF) to overcome these two challenges. Firstly, the adaptive-switch decision
tree is designed to use noisesensitive features to conduct weighted
classification and noiseinvariant features to conduct binary classification,
which makes our detector perform more robust to noise. Secondly, considering
the multiscale property that the sparsity of the 2D point cloud is proportional
to the length of laser beams, we design a multiscale random forest structure to
detect legs at different distances. Moreover, the proposed approach allows us
to discover a sparser human leg from point clouds than others. Consequently,
our method shows an improved performance compared to other state-of-the-art leg
detectors on the challenging Moving Legs dataset and retains the whole pipeline
at a speed of 60+ FPS on lowcomputational laptops. Moreover, we further apply
the proposed MARF to the people detection and tracking system, achieving a
considerable gain in all metrics.
