Share Email Print
cover

Proceedings Paper • new

Cloud Chaser: real time deep learning computer vision on low computing power devices
Author(s): Zhengyi Luo; Austin Small; Liam Dugan ; Stephen Lane
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

Internet of Things (IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time critical services such as emergency response, home assistance, surveillance, etc., these devices often need real time analysis of their camera data. This paper strives to offer a viable approach to integrate high performance deep learning based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real time vision tasks. Furthermore, to reduce latency and improve real time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412Q (15 March 2019); doi: 10.1117/12.2523087
Show Author Affiliations
Zhengyi Luo, Univ. of Pennsylvania (United States)
Austin Small, Univ. of Pennsylvania (United States)
Liam Dugan , Univ. of Pennsylvania (United States)
Stephen Lane, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

© SPIE. Terms of Use
Back to Top