# Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices

arXiv: https://arxiv.org/abs/1810.01069 · HTML: https://mlsanity.com/paper/1810.01069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification](https://mlsanity.com/paper/2204.00943/) (2022)

## Abstract (arXiv)

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.