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Kwan, C., Chou, B., Echavarren, A., Budavari, B., Li, J. and Tran, T. (2018) Compressive Vehicle Tracking Using Deep Learning. IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference, New York, 8-10 November 2018.

has been cited by the following article:

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

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

  KEYWORDS: Target Tracking, Classification, Compressive Sensing, MWIR, LWIR, YOLO, ResNet, Infrared Videos

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.10 No.3, August 8, 2019

  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.