Title
Onboard ROI selection for aerial surveillance using a high resolution, high framerate camera
Author
Boehrer, N.
Gabriel, A.
Brandt, A.
Uijens, W.R.
Kampmeijer, L.
van der Stap, N.
Schutte, K.
Publication year
2020
Abstract
With the latest advances in image sensor technology, cameras are able to generate video with tens of megapixels per frame. These high resolution videos streams offer great potential to be used in the surveillance domain. For ground based systems, gigapixel streams are already used with great effect as illustrated by the ICME 2019 crowd counting challenge. However, for Unmanned Aerial Vehicles (UAVs), this vast stream of data exceeds the limit of transmission bandwidth to send this data back to the ground. On board data analysis and selection is thus required to use and benefit from high resolution cameras. This paper presents a result of the CAVIAR project, where a combination of hardware and algorithms was designed to answer the question: ‘how to exploit a high resolution high frame rate camera on board a UAV?’. With the associated size, weight and power limitations, we implement data reduction by deploying deep learning on hardware to find the relevant information and transmit it to an operator station. The proposed solution aims at employing the high resolution potential of the sensor only onto objects of interest. We encode and transmit the identified regions containing those objects of interest (ROI) at the original resolution and framerate, while also transmitting the downscaled background to provide context for an operator. We demonstrate using a 35 fps, 65 Megapixel camera that this set-up indeed saves considerable bandwidth while retaining all important video data at high quality at the same time.
Subject
Embedded deep learning
Object detection
High resolution imagery
UAV
ROI streaming
Defence Research
Defence, Safety and Security
To reference this document use:
http://resolver.tudelft.nl/uuid:c7b7fb36-e974-402a-aea3-966320f4cc93
TNO identifier
877960
Publisher
SPIE
Source
SPIE Defense + Commercial Sensing, 2020 Proceedings Volume 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020
Document type
conference paper