Kwan, C., Chou, B. and Kwan, L.M. (2018) A Comparative Study of Conventional and Deep Learning Target Tracking Algorithms for Low Quality Videos. 15th International Symposium on Neural Networks, Minsk, 25-28 June 2018, 521-531.
https://doi.org/10.1007/978-3-319-92537-0_60

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.