Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras  

作　　者：Chiman Kwan Bryan Chou Jonathan Yang Akshay Rangamani Trac Tran Jack Zhang Ralph Etienne-Cummings 

机构地区：[1]Applied Research LLC, Rockville, Maryland, USA [2]Google, Inc., Mountain View, California, USA [3]Department of Electrical and Computer Engineering, the Johns Hopkins University, Baltimore, USA [4]Department of Electrical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

出　　处：《Journal of Signal and Information Processing》2019年第3期73-95,共23页信号与信息处理（英文）

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.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 

分 类 号：R73[医药卫生—肿瘤]