標題: ESTHER: Joint Camera Self-Calibration and Automatic Radial Distortion Correction From Tracking of Walking Humans
作者: Tang, Zheng
Lin, Yen-Shuo
Lee, Kuan-Hui
Hwang, Jenq-Neng
Chuang, Jen-Hui
資訊工程學系
Department of Computer Science
關鍵字: Camera calibration;estimation of distribution algorithm;multiple object tracking;radial distortion correction;self-calibration;video surveillance
公開日期: 1-Jan-2019
摘要: Camera calibration and radial distortion correction are the crucial prerequisites for many applications in image big data and computer vision. Many existing works rely on the Manhattan world assumption to estimate the camera parameters automatically; however, they may perform poorly when there was lack of man-made structure in the scene. As walking humans are the common objects in video surveillance, they have also been used for camera self-calibration, but the main challenges include the noise reduction for the estimation of vanishing points, the relaxation of assumptions on unknown camera parameters, and the radial distortion correction. In this paper, we present a novel framework for camera self-calibration and automatic radial distortion correction. Our approach starts with the reliable human body segmentation that is facilitated by robust object tracking. Mean shift clustering and Laplace linear regression are, respectively, introduced in the estimation of the vertical vanishing point and the horizon line. The estimation of distribution algorithm, an evolutionary optimization scheme, is then utilized to optimize the camera parameters and the distortion coefficients, in which all the unknowns in camera projection can be fine-tuned simultaneously. Experiments on the three public benchmarks and our own captured dataset demonstrate the robustness of the proposed method. The superiority of this algorithm is also verified by the capability of reliably converting 2D object tracking into 3D space.
URI: http://dx.doi.org/10.1109/ACCESS.2019.2891224
http://hdl.handle.net/11536/148833
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2891224
期刊: IEEE ACCESS
Volume: 7
起始頁: 10754
結束頁: 10766
Appears in Collections:Articles