Paper Info
Title
Image Processing Units on Ultra-low-cost Embedded Hardware: Algorithmic Optimizations for Real-time Performance.
Abstract
The design and development of image processing units (IPUs) has traditionally involved trade-offs between cost, real-time properties, portability, and ease of programming. A standard PC can be turned into an IPU relatively easily with the help of readily available computer vision libraries, but the end result will not be portable, and may be costly. Similarly, one can use field programmable gate arrays (FPGAs) as the base for an IPU, but they are expensive and require hardware-level programming. Finally, general purpose embedded hardware tends to be under-powered and difficult to develop for due to poor support for running advanced software. In recent years a new option has surfaced: single-board computers (SBCs). These generally inexpensive embedded devices would be attractive as a platform on which to develop IPUs due to their inherent portability and good compatibility with existing computer vision (CV) software. However, whether their performance is sufficient for real-time image processing has thus far remained an open question. Most SBCs (especially the ultra-low-cost ones which we target) do not offer CUDA/OpenCL support which makes it difficult to port GPU-based CV applications. In order to utilize the full power of the SBCs, their GPUs to be used. In our attempts at doing this, we have observed that the CV algorithms which an IPU uses have to be re-designed according to the OpenGL support available on these devices. This work presents a framework where a selection of CV algorithms have been designed in a way that they optimize performance on SBCs while still maintaining portability across devices which offer OpenGL ES 2.0 support. Furthermore, this paper demonstrates an IPU based on a representative SBC (namely the Raspberry Pi) along with two CV applications backed by it. The robustness of the applications as well as the performance of the IPU are evaluated to show that SPCs can be used to build IPUs capable of producing accurate data in real time. This opens the possibilities of large scale economically deployment of vision system especially in remote and barren lands. Finally, the software developed as a part of this work has been released open source.
Year
DOI
Venue
2018
https://doi.org/10.1007/s11265-017-1267-1
Signal Processing Systems
Keywords
Field
DocType
Embedded vision,Image processing units,Human tracking
Machine vision,CUDA,Computer science,Image processing,Robustness (computer science),Software,Artificial intelligence,OpenGL,Parallel computing,Field-programmable gate array,Software portability,Machine learning,Embedded system
Journal
Volume
Issue
ISSN
90
6
1939-8018
Citations 
PageRank 
References 
0
0.34
15
Authors
5
Name
Order
Citations
PageRank
Suraj Nair1337.58
Nikhil Somani2437.34
Artur Grunau300.34
Emmanuel C. Dean-Leon46215.39
Alois Knoll Knoll51700271.32