The Morpheus project aims to build a proof of concept for an advanced, Graphics Processing Unit (GPU) based embedded vision system, enabling contactless camera sensing of humans. The purpose of contactless sensing is unobtrusive remote monitoring of people, to avoid the use of sensors fixed on the body. The sensors used nowadays introduce vulnerability during practical use of the diagnose system, stress, pain and discomfort for healthy humans or those under medical treatment.
The ultimate goal of Morpheus is to use advanced computer vision to omit the necessity of many sensors used nowadays and to improve the quality of existing traditional sensing systems. However, video information processing requires large amounts of processing. Within Morpheus, advanced parallel processing in embedded vision systems can make contactless sensing possible. The project aims to contribute as follows:
An embedded vision platform will be designed and produced, supporting the constraints of contactless sensing applications. The hardware will have to meet certain constraints, such as high throughput and low power. Therefore, the platform will be based on a massively parallel processor, the GPU.
The project will deliver a design environment used to map vision applications onto the hardware platform. This design environment brings programmability of these platforms into reach of all, even small, companies.
The two applications, baby monitoring at home and neonatal care, will be developed, mapped and tested onto the hardware platform.
Vision algorithms, specific for the sensing of vital parameters, will be designed and adjusted to map onto the embedded vision platform. This includes software sensors used for the modelling process.
The project will create a demonstrator for the platform running the two applications.
The objective of this project is the development of a fully trainable application for speed sign detection. Instead of using a complex chain of algorithms this solution uses a more general algorithm based on Convolutional Neural Networks (CNNs).
The advantages of this approach are:
A very flexible algorithm that does not require much design time due to training with examples
Possibility to add new recognition objectives by a simple weight set update
Due to the parallel nature of the algorithm it is very scalable on the next generation of computing platforms
The result of the project is a speed sign recognition application that can detect multiple speed signs in a 720×1280 HD video stream real time at 35 fps.
Demo videos that show the capabilities of the application are available on YouTube:
More details are described in our paper, check the attachment.
Embedded Vision Architecture (EVA)
Posted on 2-3-2011 by Cedric NugterenTags: architecture, application, tool, FPGA
The overall EVA project objective is:
To develop a generic Embedded Vision Architecture (EVA) based on suitable applications
To apply digital design tools to develop application specific versions of this generic Embedded Vision Architecture (EVA) template
To further develop design tools to make flexible use of FPGAs in the designs possible
To demonstrate this by the designing of offspring of the generic EVA for three applications:
1. Distributed vision for component placement machines
2. Vision in the loop for industrial ink-jet printing
3. Cooperating tracking cameras for Augmented Reality and Automated Vehicles
To realize and test the offspring and successfully integrate it in existing products