Multi-Part People Detection Using 2D Range Data

People detection is a key capacity for robotics systems that have to interact with humans. This paper addresses the problem of detecting people using multiple layers of 2D laser range scans. Each layer contains a classifier able to detect a particular body part such as a head, an upper body or a leg. These classifiers are learned using a supervised approach based on AdaBoost. The final person detector is composed of a probabilistic combination of the outputs from the different classifiers. Experimental results with real data demonstrate the effectiveness of our approach to detect persons in indoor environments and its ability to deal with occlusions. Keywords Laser-based people detection · Multiple cue classification · Sensor fusion · Multi-part object detection

• Type: Journal
• Year: 2010
• Where: IJSR
• Authors: Óscar Martínez Mozos, Ryo Kurazume, Tsutomu Hasegawa
• Keywords: IJSR 2010, Keywords Laser-based People, Laser Range Scans, final Person Detector