An Effective Multi-Camera Dataset and Hybrid Feature Matcher for Real-Time Video Stitching

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.

I. Introduction

Image/Video stitching focuses on generating wide-view, high-resolution contents from overlapping images/videos. It has been widely applied on various applications such as aerospace, automobiles, security monitoring, surveillance, virtual reality, sports broadcasting, and so on [1]. Numerous approaches have been reported in the literature, which can be categorized into three main groups, i.e., image, video, and panoramic stitching. It involves three major steps for performing image/video stitching, including (a) feature extraction and description, (b) keypoint matching, (c) transforming the matching points to a single coordinate system.