Abstract
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.
Highlights
The demand for a compact, portable camera is rapidly growing because of popularized consumer hand-held cameras with easy handling and compact size such as mobile cameras, digital cameras, digital camcorders, drone cameras, and wearable cameras
We used scale invariant feature transform (SIFT) and speeded up robust features (SURF) algorithms with threshold values used in [24,25], respectively
The proposed video stabilization method removes unstable motions by estimating the optimal camera path using the robust keypoints extraction in the textureless region, and it smooths the shaky motions without frame delay using the variational optimization method
Summary
The demand for a compact, portable camera is rapidly growing because of popularized consumer hand-held cameras with easy handling and compact size such as mobile cameras, digital cameras, digital camcorders, drone cameras, and wearable cameras. Video sequences are subject to undesired vibrations due to camera shaking caused by poor handling and/or a dynamic, unstable environment To overcome this problem, various video stabilization methods have been developed to improve the visual quality of various hand-held cameras [1]. Liu et al modeled mesh-based 2D camera motion with bundled camera path to improve the video stabilization performance [14], and Kim et al classified background feature points using the KLT tracker [15]. It is hard to implementation in real-time or near real-time service because of the high computational complexity, and these methods have the common problem of the parallax caused by feature tracking failure in flat region To solve these problems, this paper presents a novel video stabilization algorithm using a robust feature detection method to improve existing 2D methods instead of the less robust 3D methods.
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