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

MARF: Multiscale Adaptive-switch Random Forest for Leg Detection with 2D Laser Scanners