Document Type
Conference Paper
Publication Date
5-2009
Keywords
telemetry, sensor network, wireless mesn network, accelerometer, situational Awareness, canine pose estimation, canine augmentation technology
Abstract
In this paper we discuss determining canine pose in the context of common poses observed in Urban Search and Rescue dogs through the use a sensor network made up of accelerometers. We discuss the use of the Canine Pose Estimation System in a disaster environment, and propose techniques for determining canine pose. In addition we discuss the challenges with this approach in such environments. This paper presents the experimental results obtained from the Heavy Urban Search and Rescue disaster simulation, where experiments were conducted using multiple canines, which show that angles can be derived from acceleration readings. Our experiments show that similar angles were measured for each of the poses, even when measured on multiple USAR canines of varying size. We also developed an algorithm to determine poses and display the current canine pose to the screen of a laptop. The algorithm was successful in determining some poses and had difficulty with others. These results are presented and discussed in this paper.
Faculty
Faculty of Applied Science & Technology (FAST)
Journal
Canadian Conference on Computer and Robot Vision
First Page
37
Last Page
44
Copyright
© Cristina Ribeiro, Alexander Ferworn, Mieso Denko, James Tran
Terms of Use
Terms of Use for Works posted in SOURCE.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Original Publication Citation
Ribeiro, C.,Ferworn, A., Denko, M. & Tran, J. (2009). Canine pose estimation: A computing for public safety solution. Canadian Conference on Computer and Robot Vision, 37-44. 10.1109/CRV.2009.38.
SOURCE Citation
Ribeiro, Cristina; Ferworn, Alexander; Denko, Mieso; and Tran, James, "Canine Pose Estimation: A Computing for Public Safety Solution" (2009). Publications and Scholarship. 50.
https://source.sheridancollege.ca/fast_publications/50
