%0 Journal Article
%T Deep Learning Based Target Tracking and Classification for Infrared Videos Using Compressive Measurements
%A Chiman Kwan
%A Bryan Chou
%A Jonathan Yang
%A Trac Tran
%J Journal of Signal and Information Processing
%P 167-199
%@ 2159-4481
%D 2019
%I Scientific Research Publishing
%R 10.4236/jsip.2019.104010
%X Although compressive measurements save data storage
and bandwidth usage, they are difficult to be used directly for target tracking
and classification without pixel reconstruction. This is because the Gaussian
random matrix destroys the target location information in the original video
frames. This paper summarizes our research effort on target tracking and
classification directly in the compressive measurement domain. We focus on one
particular type of compressive measurement using pixel subsampling. That is,
original pixels in video frames are randomly subsampled. Even in such a special
compressive sensing setting, conventional trackers do not work in a
satisfactory manner. We propose a deep learning approach that integrates YOLO
(You Only Look Once) and ResNet (residual network) for multiple target tracking
and classification. YOLO is used for multiple target tracking and ResNet is for
target classification. Extensive experiments using short wave infrared (SWIR),
mid-wave infrared (MWIR), and long-wave infrared (LWIR) videos demonstrated the
efficacy of the proposed approach even though the training data are very
scarce.
%K Target Tracking
%K Classification
%K Compressive Sensing
%K SWIR
%K MWIR
%K LWIR
%K YOLO
%K ResNet
%K Infrared Videos
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=96711