文章基本信息

标题：Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras
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作者：Chiman Kwan ; Bryan Chou ; Jonathan Yang 等
期刊名称：Journal of Signal and Information Processing
印刷版ISSN：2159-4465
电子版ISSN：2159-4481
出版年度：2019
卷号：10
期号：3
页码：73-95
DOI：10.4236/jsip.2019.103006
出版社：Scientific Research Publishing
摘要：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..
关键词：Target Tracking;Classification;Compressive Sensing;MWIR;LWIR;YOLO;ResNet;Infrared Videos