Human leg detection from depth sensing
Andrei Lucian, Andreea Sandu, Radu Orghidan, Daniel Moldovan · 2018
Human detection in a scene is very useful in applications where the control of people flow or surveillance is required. Herein we propose a solution for person detection by counting pairs of legs using a simulation of a LIDAR from a Kinect laser scanner, and a particular instance of YOLO neural net architecture. We employed both geometrical and machine learning approaches for 2D and 3D images. We managed to achieve real-time detection of multiple people from 2D and 3D information. Our miss-rate for 3D images was as low as 3.2% and a false positive per image of 0.06 at 78 FPS.