%0 Journal Article
%T Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras
%A Chiman Kwan
%A Bryan Chou
%A Jonathan Yang
%A Akshay Rangamani
%A Trac Tran
%A Jack Zhang
%A Ralph Etienne-Cummings
%J Journal of Signal and Information Processing
%P 73-95
%@ 2159-4481
%D 2019
%I Scientific Research Publishing
%R 10.4236/jsip.2019.103006
%X <div style=\"text-align:justify;\">
	Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing
camera that has low power consumption and high compression ratio. Moreover,
a PCE camera can control individual pixel exposure time that can enable
high dynamic range. Conventional approaches of using PCE camera involve
a time consuming and lossy process to reconstruct the original frames
and then use those frames for target tracking and classification. In this paper,
we present a deep learning approach that directly performs target tracking
and classification in the compressive measurement domain without any
frame reconstruction. Our approach has two parts: tracking and classification.
The tracking has been done using YOLO (You Only Look Once) and the
classification is achieved using Residual Network (ResNet). Extensive experiments
using mid-wave infrared (MWIR) and long-wave infrared (LWIR)
videos demonstrated the efficacy of our proposed approach.
</div>
%K Target Tracking
%K Classification
%K Compressive Sensing
%K MWIR
%K LWIR
%K YOLO
%K ResNet
%K Infrared Videos
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=94197