Journal
SENSORS
Volume 19, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/s19173702
Keywords
compressive sensing; pixel-wise code exposure camera; YOLO; ResNet; target tracking; target classification; optical; MWIR
Funding
- US Air Force [FA8651-17-C-0017]
Ask authors/readers for more resources
Compressive sensing has seen many applications in recent years. One type of compressive sensing device is the Pixel-wise Code Exposure (PCE) camera, which has low power consumption and individual control of pixel exposure time. In order to use PCE cameras for practical applications, a time consuming and lossy process is needed to reconstruct the original frames. 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. In particular, we propose to apply You Only Look Once (YOLO) to detect and track targets in the frames and we propose to apply Residual Network (ResNet) for classification. Extensive simulations using low quality optical and mid-wave infrared (MWIR) videos in the SENSIAC database demonstrated the efficacy of our proposed approach.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available