Cloud Chaser: real time deep learning computer vision on low computing power devices

Zhengyi Luo; Austin Small; Liam Dugan; Stephen Lane

doi:10.1117/12.2523087

15 March 2019 Cloud Chaser: real time deep learning computer vision on low computing power devices

Zhengyi Luo, Austin Small, Liam Dugan , Stephen Lane

Author Affiliations +

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110412Q (2019) https://doi.org/10.1117/12.2523087
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

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.

Citation Download Citation

Zhengyi Luo, Austin Small, Liam Dugan , and Stephen Lane "Cloud Chaser: real time deep learning computer vision on low computing power devices", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412Q (15 March 2019); https://doi.org/10.1117/12.2523087

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