Abstract
One key advantage of compressive sensing is that only a small amount of the raw video data is transmitted or saved. This is extremely important in bandwidth constrained applications. Moreover, in some scenarios, the local processing device may not have enough processing power to handle object detection and classification and hence the heavy duty processing tasks need to be done at a remote location. Conventional compressive sensing schemes require the compressed data to be reconstructed first before any subsequent processing can begin. This is not only time consuming but also may lose important information in the process. In this paper, we present a real-time framework for processing compressive measurements directly without any image reconstruction. A special type of compressive measurement known as pixel-wise coded exposure (PCE) is adopted in our framework. PCE condenses multiple frames into a single frame. Individual pixels can also have different exposure times to allow high dynamic ranges. A deep learning tool known as You Only Look Once (YOLO) has been used in our real-time system for object detection and classification. Extensive experiments showed that the proposed real-time framework is feasible and can achieve decent detection and classification performance.
Highlights
Compressive measurements [1] are normally collected by multiplying the original vectorized image with a Gaussian random matrix
You Only Look Once (YOLO) model model is is used for both locations because it was trained for both locations
For both locations because it was trained for both locations
Summary
Compressive measurements [1] are normally collected by multiplying the original vectorized image with a Gaussian random matrix. There are some target tracking papers [17] in the literature that appear to be using compressive measurements, they are still using the original video frames for detection and tracking. We propose a real-time and deep learning based vehicle detection and classification approach in compressive measurement domain. The proposed detection and classification scheme is not new and has been used by us for some other problems, we are the first ones to apply the PCE measurements in real-time vehicle detection and classification. Since the drone may not have a powerful onboard processor to perform object detection, the PCE videos are wirelessly transmitted to a ground station for processing. We conclude our paper with some remarks for future research
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
