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A Hardware Architecture for Cell-Based Feature-Extraction and Classification Using Dual-Feature Space.

Authors :
An, Fengwei
Zhang, Xiangyu
Luo, Aiwen
Chen, Lei
Mattausch, Hans Jurgen
Source :
IEEE Transactions on Circuits & Systems for Video Technology; Oct2018, Vol. 28 Issue 10, p3086-3098, 13p
Publication Year :
2018

Abstract

Many computer-vision and machine-learning applications in robotics, mobile, wearable devices, and automotive domains are constrained by their real-time performance requirements. This paper reports a dual-feature-based object recognition coprocessor that exploits both histogram of oriented gradient (HOG) and Haar-like descriptors with a cell-based parallel sliding-window recognition mechanism. The feature extraction circuitry for HOG and Haar-like descriptors is implemented by a pixel-based pipelined architecture, which synchronizes to the pixel frequency from the image sensor. After extracting each cell feature vector, a cell-based sliding window scheme enables parallelized recognition for all windows, which contain this cell. The nearest neighbor search classifier is, respectively, applied to the HOG and Haar-like feature space. The complementary aspects of the two feature domains enable a hardware-friendly implementation of the binary classification for pedestrian detection with improved accuracy. A proof-of-concept prototype chip fabricated in a 65-nm SOI CMOS, having thin gate oxide and buried oxide layers (SOTB CMOS), with 3.22-mm2 core area achieves an energy efficiency of 1.52 nJ/pixel and a processing speed of 30 fps for $1024\times 1616$ -pixel image frames at 200-MHz recognition working frequency and 1-V supply voltage. Furthermore, multiple chips can implement image scaling, since the designed chip has image-size flexibility attributable to the pixel-based architecture. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10518215
Volume :
28
Issue :
10
Database :
Complementary Index
Journal :
IEEE Transactions on Circuits & Systems for Video Technology
Publication Type :
Academic Journal
Accession number :
132683756
Full Text :
https://doi.org/10.1109/TCSVT.2017.2726564