Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
This addresses the need for time-critical services like emergency response on IoT and mobile devices, though it is incremental as it builds on existing cloud offloading methods.
The paper tackles the problem of enabling real-time deep learning computer vision on low-power devices by offloading computation to the cloud, demonstrating this with a Raspberry Pi robot and achieving reduced latency through compression algorithms.
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.