Target tracking and classification using compressive sensing camera for SWIR videos
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11760_2019_1506_ReferencePDF.pdf
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Author(s)
Kwan, Chiman
Chou, Bryan
Yang, Jonathan
Rangamani, Akshay
Tran, Trac
Zhang, Jack
Etienne-Cummings, Ralph
Date Issued
June 7, 2019
Publisher
Springer London
Version
Author's final manuscript
Abstract
Abstract
The pixel-wise code exposure (PCE) camera is a compressive sensing camera that has several advantages, such as low power consumption and high compression ratio. Moreover, one notable advantage is the capability to control individual pixel exposure time. Conventional approaches of using PCE cameras involve a time-consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. Otherwise, conventional approaches will fail if compressive measurements are used. 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 via detection using You Only Look Once (YOLO), and the classification is achieved using residual network (ResNet). Extensive simulations using short-wave infrared (SWIR) videos demonstrated the efficacy of our proposed approach.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s11760-019-01506-4
