Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled to generate the compressive measurements. Even in such special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using optical videos in the SENSIAC database demonstrated the efficacy of the proposed approach.
Object tracking and classification in infrared videos are challenging due to large variations in illumination, target sizes, and target orientations. Moreover, if the infrared videos only generate compressive measurements, then it will be even more difficult to perform target tracking and classification directly in the compressive measurement domain, as many conventional trackers and classifiers can only handle reconstructed frames from compressive measurements. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one special type of compressive measurement using pixel subsampling. That is, the original pixels in the video frames are randomly subsampled. Even in such special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using short wave infrared (SWIR) videos demonstrated the efficacy of the proposed approach even though the training data are very scarce.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.