3,028 research outputs found
Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
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.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV
2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser
Acceleration of stereo-matching on multi-core CPU and GPU
This paper presents an accelerated version of a
dense stereo-correspondence algorithm for two different parallelism
enabled architectures, multi-core CPU and GPU. The
algorithm is part of the vision system developed for a binocular
robot-head in the context of the CloPeMa 1 research project.
This research project focuses on the conception of a new clothes
folding robot with real-time and high resolution requirements
for the vision system. The performance analysis shows that
the parallelised stereo-matching algorithm has been significantly
accelerated, maintaining 12x and 176x speed-up respectively
for multi-core CPU and GPU, compared with non-SIMD singlethread
CPU. To analyse the origin of the speed-up and gain
deeper understanding about the choice of the optimal hardware,
the algorithm was broken into key sub-tasks and the performance
was tested for four different hardware architectures
Performance evaluation of a distributed integrative architecture for robotics
The eld of robotics employs a vast amount of coupled sub-systems. These need to interact
cooperatively and concurrently in order to yield the desired results. Some hybrid algorithms
also require intensive cooperative interactions internally. The architecture proposed lends it-
self amenable to problem domains that require rigorous calculations that are usually impeded
by the capacity of a single machine, and incompatibility issues between software computing
elements. Implementations are abstracted away from the physical hardware for ease of de-
velopment and competition in simulation leagues. Monolithic developments are complex, and
the desire for decoupled architectures arises. Decoupling also lowers the threshold for using
distributed and parallel resources. The ability to re-use and re-combine components on de-
mand, therefore is essential, while maintaining the necessary degree of interaction. For this
reason we propose to build software components on top of a Service Oriented Architecture
(SOA) using Web Services. An additional bene t is platform independence regarding both
the operating system and the implementation language. The robot soccer platform as well
as the associated simulation leagues are the target domain for the development. Furthermore
are machine vision and remote process control related portions of the architecture currently
in development and testing for industrial environments. We provide numerical data based on
the Python frameworks ZSI and SOAPpy undermining the suitability of this approach for the
eld of robotics. Response times of signi cantly less than 50 ms even for fully interpreted,
dynamic languages provides hard information showing the feasibility of Web Services based
SOAs even in time critical robotic applications
Robocart Machine Vision Framework
This project documents a framework for an extensible, flexible machine vision software implementation for the Robocart project. It uses a distributed mobile computing framework in order to best leverage the scalability of machine vision. This process aims to improve upon current machine vision implementations in commercial autonomous vehicles, as well as provide a basis for further development of RobocartÂ’s autonomous navigation systems. This framework is tested with the use case of road detection
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