Share Email Print
cover

Proceedings Paper

Autonomous target tracking of UAVs based on low-power neural network hardware
Author(s): Wei Yang; Zhanpeng Jin; Clare Thiem; Bryant Wysocki; Dan Shen; Genshe Chen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Detecting and identifying targets in unmanned aerial vehicle (UAV) images and videos have been challenging problems due to various types of image distortion. Moreover, the significantly high processing overhead of existing image/video processing techniques and the limited computing resources available on UAVs force most of the processing tasks to be performed by the ground control station (GCS) in an off-line manner. In order to achieve fast and autonomous target identification on UAVs, it is thus imperative to investigate novel processing paradigms that can fulfill the real-time processing requirements, while fitting the size, weight, and power (SWaP) constrained environment. In this paper, we present a new autonomous target identification approach on UAVs, leveraging the emerging neuromorphic hardware which is capable of massively parallel pattern recognition processing and demands only a limited level of power consumption. A proof-of-concept prototype was developed based on a micro-UAV platform (Parrot AR Drone) and the CogniMemTMneural network chip, for processing the video data acquired from a UAV camera on the y. The aim of this study was to demonstrate the feasibility and potential of incorporating emerging neuromorphic hardware into next-generation UAVs and their superior performance and power advantages towards the real-time, autonomous target tracking.

Paper Details

Date Published: 22 May 2014
PDF: 9 pages
Proc. SPIE 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, 91190P (22 May 2014); doi: 10.1117/12.2054049
Show Author Affiliations
Wei Yang, Binghamton Univ. (United States)
Zhanpeng Jin, Binghamton Univ. (United States)
Clare Thiem, Air Force Research Lab. (United States)
Bryant Wysocki, Air Force Research Lab. (United States)
Dan Shen, Intelligent Fusion Technology, Inc. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)


Published in SPIE Proceedings Vol. 9119:
Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII
Misty Blowers; Jonathan Williams, Editor(s)

© SPIE. Terms of Use
Back to Top