  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.
