Pose invariant people detection in point clouds for mobile robots

Date

2020-05-01

Author

Hacinecipoglu, Akif
Konukseven, Erhan İlhan
Koku, Ahmet Buğra

Metadata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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To be able to navigate in socially complaint fashion and safely, people detection is a very important ability for robots deployed in our social environments. However, it is a challenging task since humans exhibit various poses in daily life as they bend, sit down, touch or interact with each other. A robust people detector should detect humans also in these arbitrary poses. In addition, mobile robots should be able to carry out detection in a real-time manner because our environment is highly dynamic. In this study we developed a fast head and people detector which can, pose invariantly, detect people. Method depends only on depth information of point clouds taken from RGB-D sensors. As a result, it is robust against sudden light and contrast changes. The algorithm runs relying only on CPU, which makes it applicable to mobile robots with low computational resources.

Subject Keywords

Head detection, Robot vision, Point clouds

URI

https://hdl.handle.net/11511/38890

Journal

International Journal of Mechanical Engineering and Robotics Research

DOI

https://doi.org/10.18178/ijmerr.9.5.709-715

Collections

Department of Mechanical Engineering, Article

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Citation Formats

A. Hacinecipoglu, E. İ. Konukseven, and A. B. Koku, “Pose invariant people detection in point clouds for mobile robots,” International Journal of Mechanical Engineering and Robotics Research, pp. 709–715, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38890.