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3D people detection in domestic environments

  • The ability of detecting people has become a crucial subtask, especially in robotic systems which aim an application in public or domestic environments. Robots already provide their services e.g. in real home improvement markets and guide people to a desired product. In such a scenario many robot internal tasks would benefit from the knowledge of knowing the number and positions of people in the vicinity. The navigation for example could treat them as dynamical moving objects and also predict their next motion directions in order to compute a much safer path. Or the robot could specifically approach customers and offer its services. This requires to detect a person or even a group of people in a reasonable range in front of the robot. Challenges of such a real-world task are e.g. changing lightning conditions, a dynamic environment and different people shapes. In this thesis a 3D people detection approach based on point cloud data provided by the Microsoft Kinect is implemented and integrated on mobile service robot. A Top-Down/Bottom-Up segmentation is applied to increase the systems flexibility and provided the capability to the detect people even if they are partially occluded. A feature set is proposed to detect people in various pose configurations and motions using a machine learning technique. The system can detect people up to a distance of 5 meters. The experimental evaluation compared different machine learning techniques and showed that standing people can be detected with a rate of 87.29% and sitting people with 74.94% using a Random Forest classifier. Certain objects caused several false detections. To elimante those a verification is proposed which further evaluates the persons shape in the 2D space. The detection component has been implemented as s sequential (frame rate of 10 Hz) and a parallel application (frame rate of 16 Hz). Finally, the component has been embedded into complete people search task which explorates the environment, find all people and approach each detected person.

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Metadaten
Document Type:Report
Language:English
Author:Frederik Hegger
Pagenumber:91
ISBN:978-3-96043-008-7
ISSN:1869-5272
URN:urn:nbn:de:hbz:1044-opus-107
DOI:https://doi.org/10.18418/978-3-96043-008-7
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2012/03/15
Series (Volume):Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences, Department of Computer Science (02-2012)
GND Keyword:3D-Scanner
Departments, institutes and facilities:Fachbereich Informatik
Entry in this database:2012/03/15
Licence (Multiple languages):License LogoIn Copyright (Urheberrechtsschutz)