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 d...Show MoreMetadata
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.
Date of Conference: 09-10 December 2021
Date Added to IEEE Xplore: 29 December 2021
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Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey
Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey
Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey
Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey
Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey
Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey