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Proceedings Paper

Drone based user and heading detection using deep learning and stereo vision
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Paper Abstract

The problem of assisting a low-vision person with environment awareness using a drone is addressed. Specifically, the first stage task of detecting the User and their heading which does not require any user adaptive training is tackled. The modalities of 3D and 2D vision on a drone are compared for this task. 3D data is provided using a stereo sensor mounted on the drone that communicates using RF to a mobile device based android application. For the task of localization, a Single Shot MulitBox Detector is utilized. Different networks in terms of input modalities and related structure are developed including a 2D only network and a 3D+2D fused network. Performance of these networks are compared and results discused. In addition, a comparison of retrained networks versus training from scratch is made. In all cases, approximately 34,000 user heading samples were collected for training. Real data from outdoor drone flights that communicate with the Android based application are shown. Detecting both the user in the scene and their heading is an important first step necessary in a drone-based system that helps low-vision persons with environment awareness. The success and challenges faced are presented along with future avenues of work.

Paper Details

Date Published: 7 May 2019
PDF: 14 pages
Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 110180X (7 May 2019); doi: 10.1117/12.2518266
Show Author Affiliations
Lynne Grewe, California State Univ., East Bay (United States)
Garrett Stevenson, California State Univ., East Bay (United States)


Published in SPIE Proceedings Vol. 11018:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII
Ivan Kadar; Erik P. Blasch; Lynne L. Grewe, Editor(s)

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