Detection and Localization of Multiple Objects

Author

Zickler, Stefan ; Veloso, Manuela M.

Author_Institution

Dept. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA

fYear

2006

Firstpage

20

Lastpage

25

Abstract

Being able to identify and localize objects is an important requirement for various humanoid robot applications. In this paper we present a method which uses PCA-SIFT in combination with a clustered voting scheme to achieve detection and localization of multiple objects in real-time video data. Our approach provides robustness against constraints that are common for humanoid vision systems such as perspective changes, partial occlusion, and motion blurring. We analyze and evaluate the performance of our method in two concrete humanoid test-scenarios

Keywords

SLAM (robots); feature extraction; humanoid robots; object detection; object recognition; principal component analysis; robot vision; transforms; humanoid robot; humanoid vision system; motion blurring; object detection; object identification; object localization; partial occlusion; principal component analysis; scale invariant feature transform; Application software; Computer science; Humanoid robots; Legged locomotion; Object detection; Object recognition; Robot vision systems; Robustness; Video compression; Voting;

fLanguage

English

Publisher

ieee

Conference_Titel

Humanoid Robots, 2006 6th IEEE-RAS International Conference on

Conference_Location

Genova

Print_ISBN

1-4244-0200-X

Electronic_ISBN

1-4244-0200-X

Type

conf

DOI

10.1109/ICHR.2006.321358

Filename

4115575