Open AccessVery large‐scale integration architecture for video stabilisation and implementation on a field programmable gate array‐based autonomous vehicle
Author(s) -
NouShene Tahiyah,
Pudi Vikramkumar,
Sridharan K.,
Thomas Vineetha,
Arthi J.
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0120
Subject(s) - field programmable gate array , computer science , verilog , gate array , sobel operator , computer vision , artificial intelligence , computer hardware , embedded system , image processing , edge detection , image (mathematics)
Autonomous vehicles engaged in terrain exploration are typically equipped with a camera. The camera is subjected to vibration as the vehicle moves so that the videos captured require stabilisation to facilitate accurate interpretation by remote operators. Dedicated architectures for video stabilisation that offer high performance while consuming low area and power are desirable for this application. This study presents a pipelined very large‐scale integration architecture. It is based on exploiting the separability property of the two‐dimensional (2‐D) Sobel matrix and the 2‐D Gaussian filtering matrix to obtain an efficient corner point detection architecture. It also employs the coordinate rotation digital computer architecture for global motion vector calculation. The proposed architecture has been coded in Verilog and synthesised for a field programmable gate array (FPGA), which offers massive parallelism at fairly low power. The proposed architecture is shown to be highly area efficient. An FPGA‐based autonomous vehicle has been fabricated, and experiments with a camera mounted on the vehicle are presented and analysed.