Deep Learning-Based Target Tracking and Classification for Low Quality Videos Using Coded Aperture Cameras

Sensors

Chiman Kwan¹  Bryan Chou¹  Jack Zhang²  Ralph Etienne-Cummings³  Akshay Rangamani³  Trac Tran³  Jonathan Yang⁴ 

[1] Applied Research LLC, Rockville, MD 20850, USA;Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02138, USA;Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA;Google, Inc., Mountain View, CA 94043, USA;

关键词: compressive sensing;    pixel-wise code exposure camera;    YOLO;    ResNet;    target tracking;    target classification;    optical;    MWIR;   

DOI  :  10.3390/s19173702

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown