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학술대회 Parallax-tolerant Video Stitching with Moving Foregrounds
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저자
무하마드, 조용주, 서정일
발행일
201911
출처
Asian Conference on Pattern Recognition (ACPR) 2019 (LNCS 12047), pp.1-14
DOI
https://dx.doi.org/10.1007/978-3-030-41299-9_49
협약과제
19ZR1100, 초실감 공간미디어 원천기술 개발, 서정일
초록
The parallax artifacts introduced due to movement of objects across different views in the overlapping area drastically degrade the video stitching quality. To alleviate such visual artifacts, this paper extend our earlier video stitching framework혻[1] by replacing a deep learning based object detection algorithm for parallax detection, and an optical flow estimation algorithm for parallax correction. Given a set of multi-view overlapping videos, the geometric look-up tables (G-LUT) are generated by stitching a reference frame from the multi-view input videos, which map the input video frames to the panorama domain. We propose to use a deep learning based approach to detect the moving objects in the overlapping area to identify the G-LUT control points which get affected by parallax. To compute the optimal locations of these parallax affected G-LUT control points we propose to use patch-match based optical flow (CPM-flow). The adjustment of G-LUT control points in the overlapping area may cause some unwanted geometric distortions in the non-overlapping area. Therefore, the G-LUT control points in close proximity of moving objects are also updated to ensure the smooth geometric transition between the overlapping and the non-overlapping area. Experimental results on challenging video sequences with very narrow overlapping areas (~3% to ~10%) demonstrate that video stitching framework with the proposed parallax minimization scheme can significantly suppress the parallax artifacts occurring due to the moving objects. In comparison to our previous work, the computational time is reduce by ~26% with the proposed scheme, while the stitching quality is also marginally improved.
키워드
Object detection, Optical flow, Parallax, Video stitching
KSP 제안 키워드
Computational time, Control Points, Detection algorithm, Geometric Distortion, Moving Object, Multi-view, Object detection, Optical flow estimation, Patch-match, Reference frame, Video Stitching