Multi-Frame Based Homography Estimation for Video Stitching in Static Camera Environments

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 1; p. 92
• Main Authors: Park, Keon-Woo, Shim, Yoo-Jeong, Lee, Myeong-Jin, Ahn, Heejune
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.12.2019; MDPI
• Subjects: Accuracy; Algorithms; Cameras; Distortion; homography estimation; Methods; multi-frame based homography; Noise; Radiation; representative feature point; Stitching; video alignment; video stitching; video alignment; representative feature point; multi-frame based homography; video stitching; homography estimation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20010092

In this paper, a multi-frame based homography estimation method is proposed for video stitching in static camera environments. A homography that is robust against spatio-temporally induced noise can be estimated by intervals, using feature points extracted during a predetermined time interval. The feature point with the largest blob response in each quantized location bin, a representative feature point, is used for matching a pair of video sequences. After matching representative feature points from each camera, the homography for the interval is estimated by random sample consensus (RANSAC) on the matched representative feature points, with their chances of being sampled proportional to their numbers of occurrences in the interval. The performance of the proposed method is compared with that of the per-frame method by investigating alignment distortion and stitching scores for daytime and noisy video sequence pairs. It is shown that alignment distortion in overlapping regions is reduced and the stitching score is improved by the proposed method. The proposed method can be used for panoramic video stitching with static video cameras and for panoramic image stitching with less alignment distortion.