Jeon Semi, Yoon Inhye, Jang Jinbeum, Yang Seungji, Kim Jisung, Paik Joonki
Department of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea.
ADAS Camera Team, LG Electronics, 322 Gyeongmyeong-daero, Seo-gu, Incheon 22744, Korea.
Sensors (Basel). 2017 Feb 10;17(2):337. doi: 10.3390/s17020337.
Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i) robust feature detection using particle keypoints between adjacent frames; (ii) camera path estimation and smoothing; and (iii) rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV). The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems.
获取稳定的视频是各类数码相机的一个重要问题。本文提出了一种自适应相机路径估计方法，该方法利用鲁棒特征检测来去除视频中的抖动伪影。所提出的算法包括三个步骤：(i) 使用相邻帧之间的粒子关键点进行鲁棒特征检测；(ii) 相机路径估计和平滑；(iii) 渲染以重建稳定的视频。结果表明，该算法可以通过使用粒子关键点重新定义平坦区域中的重要特征点来估计最优单应性。此外，可以从使时间总变差 (TV) 最小化的最优自适应相机路径生成孔洞较少的稳定帧。所提出的视频稳定方法适用于提高各种便携式相机的视觉质量，并且可以应用于机器人视觉、驾驶辅助系统和视觉监控系统。
