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

• 作者: Akshay Rangamani Bryan Chou Chiman Kwan Jack Zhang Jonathan Yang Ralph Etienne-Cummings Trac Tran
• 来源期刊: 信号与信息处理(英文)
• 学科: 医学
• 关键词: Target Tracking Classification COMPRESSIVE Sensing MWIR LWIR YOLO ResNet INFRARED VIDEOS
• 年，卷（期）: xhyxxclyw_2019,（3）
• 页码范围: 73-95
• 页数: 23页
• 分类号: R73

摘要

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.