@inproceedings{34fde862ac87418794f3820cb044b7bf,
title = "An Effective Multi-Camera Dataset and Hybrid Feature Matcher for Real-Time Video Stitching",
abstract = "Multi-camera video stitching combines several videos captured by different cameras into a single video for a wide Field-of-View (FOV). In this paper, a novel dataset is developed for video stitching which consists of 30 video sets captured by four static cameras in various environmental scenarios. Then, a new video stitching method is proposed based on a hybrid matcher for stitching four videos with over 200° FOV. The keypoints and descriptors are obtained by the scale-invariant feature transform (SIFT) and Root-SIFT, respectively. Then, these keypoint descriptors are matched by applying a hybrid matcher, a combination of Brute Force (BF), and Fast Linear Approximated Nearest Neighbours (FLANN) matchers. After geometrical verification and eliminating outlier matching points, one-time homography is estimated based on Random Sample Consensus (RANSAC). The proposed method is implemented and evaluated in different indoor/outdoor video settings. Experimental results demonstrate the capability, high accuracy, and robustness of the proposed method.",
keywords = "Feature matcher, Image stitching, Multi-camera video dataset, RANSAC, SIFT, Video stitching",
author = "{Imran Hosen}, Md and {Baharul Islam}, Md and Arezoo Sadeghzadeh",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 36th International Conference on Image and Vision Computing New Zealand, IVCNZ 2021 ; Conference date: 09-12-2021 Through 10-12-2021",
year = "2021",
doi = "10.1109/IVCNZ54163.2021.9653352",
language = "English",
series = "International Conference Image and Vision Computing New Zealand",
publisher = "IEEE Computer Society",
editor = "Cree, {Michael J.}",
booktitle = "Proceedings of the 2021 36th International Conference on Image and Vision Computing New Zealand, IVCNZ 2021",
}