Abstract. This paper addresses the problem of lateral chromatic aberration correction in images through color planes warping. We aim at high precision (largely sub-pixel) realignment of color channels. This is achieved thanks to two ingredients: high precision keypoint detection, which in our case are disk centers, and more general correction model than what is commonly used in the literature, radial polynomial. Our setup is quite easy to implement, requiring a pattern of black disks on white paper and a single snapshot. We measure the errors in terms of geometry and of color and compare our method to three different software programs. Quantitative results on real images show that our method allows alignment of average 0.05 pixel of color channels and a residual color error divided by a factor 3 to 6.
To cite this version:Victoria Rudakova, Pascal Monasse. Camera matrix calibration using circular control points and separate correction of the geometric distortion field. IEEE. Abstract-We achieve a precise camera calibration using circular control points by, first, separation of the lens distortion parameters from other camera parameters and computation of the distortion field in advance by using a calibration harp. Second, in order to compensate for perspective bias, which is prone to occur when using a circled pattern, we incorporate conic affine transformation into the minimization error when estimating the homography, and leave all the other calibration steps as they are used in the literature. Such an error function allows to compensate for the perspective bias. Combined with precise keypoint detection, the approach is shown to be more stable than current state-of-the-art global calibration method.
Object tracking is a very active research field nowadays due to its numerous potential applications in video surveillance, e.g. human activity analysis, traffic monitoring. Recently the focus has been on multi-target tracking (MTT). While considering multiple target tracking applications, 'multitarget occlusion' is a common and challenging problem that needs to be addressed. Most of the proposed solutions for this problem require camera calibration parameters that make them impractical for outdoor video surveillance applications. To address this problem we propose in this paper a probabilistic approach that does not require camera calibration for multiple camera collaboration. Initial simulation results prove the validity of the proposed approach.
Multi-target tracking is a active research field nowadays due to its wide practical applicability in video processing. While talking about Multi-target tracking, 'multi-target occlusion' is a common problem that needs to be addressed. Lots of work has been done using multiple cameras for handling 'multitarget occlusion'; however most of them require camera calibration parameters that make them impractical for outdoor video surveillance applications. The main focus of this paper is to reduce the dependency on camera calibration for multiple camera collaboration. In this perspective Gale-Shapley algorithm (GSA) has been used for finding stable matching between two or more camera views, while more robustness on tracking of objects has been ensured by combining multiple cues such object's boundary information of the object with color histogram. Efficient tracking of object ensures proficient reckoning of target depicting parameter (i.e. apparent color, height and width information of the object) as a consequence better camera collaboration. The simulation results prove the validity of our approach.
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