Research ArticleOpen AccessGoogle Scholar indexed
Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras
Applied Research LLC, Rockville, Maryland, USA
Applied Research LLC, Rockville, Maryland, USA
Google, Inc., Mountain View, California, USA
Department of Electrical and Computer Engineering, the Johns Hopkins University, Baltimore, USA
Department of Electrical and Computer Engineering, the Johns Hopkins University, Baltimore, USA
Department of Electrical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Department of Electrical and Computer Engineering, the Johns Hopkins University, Baltimore, USA
- 1 Applied Research LLC, Rockville, Maryland, USA
- 2 Applied Research LLC, Rockville, Maryland, USA
- 3 Google, Inc., Mountain View, California, USA
- 4 Department of Electrical and Computer Engineering, the Johns Hopkins University, Baltimore, USA
- 5 Department of Electrical and Computer Engineering, the Johns Hopkins University, Baltimore, USA
- 6 Department of Electrical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- 7 Department of Electrical and Computer Engineering, the Johns Hopkins University, Baltimore, USA
Journal of Signal and Information Processing·Volume 10 (2019)·Pages 73–95·Published 8 August 2019·DOI10.4236/jsip.2019.103006
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Abstract
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.
KeywordsTarget TrackingClassificationCompressive SensingMWIRLWIRYOLOResNetInfrared Videos
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