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Optical sensor-based object detection for autonomous robots

Title
Optical sensor-based object detection for autonomous robots
Authors
Kim, JonghwanLee, Chung-HeeYoung-Chul, Division of IT-Convergence, Daegu Gyeongbuk Institute of Science and Technology, Daegu,Kwon, SoonPark, Chi-Ho
DGIST Authors
Kim, Jonghwan; Lee, Chung-Hee; Young-Chul, Division of IT-Convergence, Daegu Gyeongbuk Institute of Science and Technology, Daegu,; Kwon, SoonPark, Chi-Ho
Issue Date
2011
Citation
2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2011, 746-752
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
Abstract
In this paper, we introduce objects detection optical sensors (CCD sensor: Charge Coupled Device). For autonomous robot, the location of around-object is very important because robot should avoid it for driving. In the field of computer vision research, the various object detection methods have been used by research engineers. In particular, the combination of Haar-like feature and AdaBoost classifier is a popular method for object detection. It has been used for face detection, but performs well for other object detection too. So it has become the choice of many researchers in the intelligent autonomous robot field. It is prone, however, to yield many false-positive results and use excessive processing time. We propose a solution for overcoming this limitation. We begin by normalizing the image database to improve the accuracy of classification. And optimizing AdaBoost training allows us to get the short computing time and accurate detection. Our experiments prove the superiority of the proposed method. © 2011 IEEE.
URI
http://hdl.handle.net/20.500.11750/3919
DOI
10.1109/URAI.2011.6146002
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers
Division of IoT∙Robotics Convergence Research2. Conference Papers


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