AirMatch: An Automated Mosaicing System with Video Preprocessing Engine for Multiple Aerial Feeds

Nida RASHEED  Waqar S. QURESHI  Shoab A. KHAN  Manshoor A. NAQVI  Eisa ALANAZI  
[Paper on system development]

IEICE TRANSACTIONS on Information and Systems   Vol.E104-D    No.4    pp.490-499
Publication Date: 2021/04/01
Publicized: 2021/01/14
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDK0003
Type of Manuscript: PAPER
Category: Software System
legacy systems,  remote sensing,  video mosaicing,  unmanned aerial vehicles,  video stabilization,  

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Surveillance through aerial systems is in place for years. Such systems are expensive, and a large fleet is in operation around the world without upgrades. These systems have low resolution and multiple analog cameras on-board, with Digital Video Recorders (DVRs) at the control station. Generated digital videos have multi-scenes from multi-feeds embedded in a single video stream and lack video stabilization. Replacing on-board analog cameras with the latest digital counterparts requires huge investment. These videos require stabilization and other automated video analysis prepossessing steps before passing it to the mosaicing algorithm. Available mosaicing software are not tailored to segregate feeds from different cameras and scenes, automate image enhancements, and stabilize before mosaicing (image stitching). We present "AirMatch", a new automated system that first separates camera feeds and scenes, then stabilize and enhance the video feed of each camera; generates a mosaic of each scene of every feed and produce a super quality mosaic by stitching mosaics of all feeds. In our proposed solution, state-of-the-art video analytics techniques are tailored to work on videos from vintage cameras in aerial applications. Our new framework is independent of specialized hardware requirements and generates effective mosaics. Affine motion transform with smoothing Gaussian filter is selected for the stabilization of videos. A histogram-based method is performed for scene change detection and image contrast enhancement. Oriented FAST and rotated BRIEF (ORB) is selected for feature detection and descriptors in video stitching. Several experiments on a number of video streams are performed and the analysis shows that our system can efficiently generate mosaics of videos with high distortion and artifacts, compared with other commercially available mosaicing software.