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

doi:10.1587/transinf.2020EDK0003

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

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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.

Publication: IEICE TRANSACTIONS on Information 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

Authors

Nida RASHEED
  NUST College of Electrical and Mechanical Engineering
Waqar S. QURESHI
  NUST College of Electrical and Mechanical Engineering
Shoab A. KHAN
  NUST College of Electrical and Mechanical Engineering
Manshoor A. NAQVI
  NUST College of Electrical and Mechanical Engineering
Eisa ALANAZI
  Umm Al Qura University

Keyword

legacy systems, remote sensing, video mosaicing, unmanned aerial vehicles, video stabilization

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Nida RASHEED, Waqar S. QURESHI, Shoab A. KHAN, Manshoor A. NAQVI, Eisa ALANAZI, "AirMatch: An Automated Mosaicing System with Video Preprocessing Engine for Multiple Aerial Feeds" in IEICE TRANSACTIONS on Information, vol. E104-D, no. 4, pp. 490-499, April 2021, doi: 10.1587/transinf.2020EDK0003.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDK0003/_p

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@ARTICLE{e104-d_4_490,
author={Nida RASHEED, Waqar S. QURESHI, Shoab A. KHAN, Manshoor A. NAQVI, Eisa ALANAZI, },
journal={IEICE TRANSACTIONS on Information},
title={AirMatch: An Automated Mosaicing System with Video Preprocessing Engine for Multiple Aerial Feeds},
year={2021},
volume={E104-D},
number={4},
pages={490-499},
abstract={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.},
keywords={},
doi={10.1587/transinf.2020EDK0003},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - AirMatch: An Automated Mosaicing System with Video Preprocessing Engine for Multiple Aerial Feeds
T2 - IEICE TRANSACTIONS on Information
SP - 490
EP - 499
AU - Nida RASHEED
AU - Waqar S. QURESHI
AU - Shoab A. KHAN
AU - Manshoor A. NAQVI
AU - Eisa ALANAZI
PY - 2021
DO - 10.1587/transinf.2020EDK0003
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2021
AB - 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.
ER -