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.