CVAIGRAug 17, 2023

GPU Accelerated Color Correction and Frame Warping for Real-time Video Stitching

Harvard
arXiv:2308.09209v11 citationsh-index: 18
Originality Incremental advance
AI Analysis

This work addresses video stitching challenges for applications like surveillance or virtual reality, but it is incremental as it builds on existing color correction and warping techniques.

The paper tackles the problem of temporal flickering and color inconsistency in video stitching by proposing a GPU-accelerated system that uses a spatio-temporal 3D-Matrix color correction method and optical flow-based warping, achieving real-time generation of high-quality panoramic videos.

Traditional image stitching focuses on a single panorama frame without considering the spatial-temporal consistency in videos. The straightforward image stitching approach will cause temporal flicking and color inconstancy when it is applied to the video stitching task. Besides, inaccurate camera parameters will cause artifacts in the image warping. In this paper, we propose a real-time system to stitch multiple video sequences into a panoramic video, which is based on GPU accelerated color correction and frame warping without accurate camera parameters. We extend the traditional 2D-Matrix (2D-M) color correction approach and a present spatio-temporal 3D-Matrix (3D-M) color correction method for the overlap local regions with online color balancing using a piecewise function on global frames. Furthermore, we use pairwise homography matrices given by coarse camera calibration for global warping followed by accurate local warping based on the optical flow. Experimental results show that our system can generate highquality panorama videos in real time.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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