Low-cost dedicated hardware IP modules for background subtraction in embedded vision systems

2014 ◽  
Vol 12 (4) ◽  
pp. 681-695 ◽  
Author(s):  
Elisa Calvo-Gallego ◽  
Piedad Brox ◽  
Santiago Sánchez-Solano
Keyword(s):  
Background Subtraction ◽  
Low Cost ◽  
Vision Systems ◽  
Embedded Vision ◽  
Dedicated Hardware ◽  
Embedded Vision Systems

Biologically Inspired Components in Embedded Vision Systems

2018 ◽  
pp. 458-493
Author(s):  
Li-Minn Ang ◽  
Kah Phooi Seng ◽  
Christopher Wing Hong Ngau
Keyword(s):  
Vision System ◽  
Low Complexity ◽  
Human Vision ◽  
Primary Concern ◽  
Resource Constrained ◽  
Vision Systems ◽  
Biologically Inspired ◽  
Embedded Vision ◽  
Field Programmable ◽  
Embedded Vision Systems

Biological vision components like visual attention (VA) algorithms aim to mimic the mechanism of the human vision system. Often VA algorithms are complex and require high computational and memory requirements to be realized. In biologically-inspired vision and embedded systems, the computational capacity and memory resources are of a primary concern. This paper presents a discussion for implementing VA algorithms in embedded vision systems in a resource constrained environment. The authors survey various types of VA algorithms and identify potential techniques which can be implemented in embedded vision systems. Then, they propose a low complexity and low memory VA model based on a well-established mainstream VA model. The proposed model addresses critical factors in terms of algorithm complexity, memory requirements, computational speed, and salience prediction performance to ensure the reliability of the VA in a resource constrained environment. Finally a custom softcore microprocessor-based hardware implementation on a Field-Programmable Gate Array (FPGA) is used to verify the implementation feasibility of the presented model.

Enhancing image processing architecture using deep learning for embedded vision systems

2020 ◽  
Vol 76 ◽  
pp. 103094 ◽  
Author(s):  
R. Udendhran ◽  
M. Balamurugan ◽  
A. Suresh ◽  
R. Varatharajan
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Vision Systems ◽  
Embedded Vision ◽  
Processing Architecture ◽  
Embedded Vision Systems

scholarly journals Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking

2020 ◽  
Author(s):  
Dominika Przewlocka ◽  
Mateusz Wasala ◽  
Hubert Szolc ◽  
Krzysztof Blachut ◽  
Tomasz Kryjak
Keyword(s):  
Neural Network ◽  
Object Tracking ◽  
Real Time ◽  
Video Stream ◽  
Visual Object ◽  
Vision Systems ◽  
Embedded Vision ◽  
Network Training ◽  
Binary Networks ◽  
Embedded Vision Systems

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations-from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover , quantisation of weights positively affects the network training by decreasing overfitting.

scholarly journals Development of the Embedded Vision System: A Survey

2009 ◽  
Vol 42 (7) ◽  
pp. 216-221 ◽  
Author(s):  
Wei Zou ◽  
Junzhi Yu ◽  
De Xu
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Vision Systems ◽  
Embedded Vision ◽  
System A ◽  
Processing Ability ◽  
The Future ◽  
Embedded Vision System ◽  
And Performance ◽  
Embedded Vision Systems

Recently embedded technology has been widely applied to machine vision and embedded vision systems are more and more popular. This paper reviews the advances on embedded vision systems, and then compares and analyzes their frameworks in processing ability, cost and performance. A discussion is provided for some unsolved problems for embedded vision systems. Finally, the future of embedded vision system is outlined.

Hardware Considerations for Embedded Vision Systems

2008 ◽  
pp. 3-26 ◽  
Author(s):  
Mathias Kölsch ◽  
Steven Butner
Keyword(s):  
Vision Systems ◽  
Embedded Vision ◽  
Embedded Vision Systems
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