A calibration method for paracatadioptric camera from sphere images

For paracatadioptric camera, the estimation of intrinsic parameters from sphere images is still an open and challenging problem. In this paper, we propose a calibration method for paracatadioptric camera based on sphere images, which only requires that the projected contour of parabolic mirror is visible on the image plane in one view. We have found that, under central catadioptric camera, a sphere is projected to two conics on the image plane, which are defined as a pair of antipodal sphere images. The conic that is visible on the image plane is called the sphere image, while the other invisible conic is called the antipodal sphere image. In the other aspect, according to the image formation of central catadioptric camera, these two conics can also be considered as the projections of two parallel circles on the viewing sphere by a virtue camera. That is to say, if three pairs of antipodal sphere images are known, central catadioptric camera can be directly calibrated by the calibration method based on two parallel circles. Therefore, the problem of calibrating central catadioptric camera is transferred to the estimations of sphere images and their antipodal sphere images. Based on this idea, we first initialize the intrinsic parameters of the camera by the projected contour of parabolic mirror, and use them to initialize the antipodal sphere images. Next, we study properties of several pairs of antipodal sphere images under paracatadioptric camera. Then, these properties are used to optimize sphere images and their antipodal sphere images, so as to calibrate the paracatadioptric camera. Experimental results on both simulated and real image data have demonstrated the effectiveness of our method.     

On the sensitivity analysis of camera calibration from images of spheres

This paper presents a novel sensitivity analysis of camera calibration from images of spheres. We improve the accuracy of a conic matrix and hence the accuracy of calibration results by eliminating the ambiguity of the conic orientation that arises from the nature of a circular-ellipse. In addition, relationships between the difference in length of the long and short axes of a conic and other parameters of the conic and the camera are investigated and demonstrated through parametric study and experimental analysis. By utilizing these relationships, we establish novel guidelines that can be followed to obtain better calibration results.     

一种新的基于散焦图像的摄像机标定方法

提出一种新的用于散焦求深度的摄像机内参数标定算法。该算法依靠改变摄像机镜头光圈指数获取同一场景的任意两幅散焦程度不同的图像,提取两幅图像间模糊程度差异信息,结合分析透镜成像几何标定出摄像机的相应内参数。此算法解除了2006年由Park所提出的标定方法中必须有一幅聚焦图像的限制,并且无须对图像进行复杂的放大率标准化处理。模拟实验与真实实验均验证了算法的有效性和精确性。     

Robust refinement methods for camera calibration and 3D reconstruction from multiple images

This paper proposes robust refinement methods to improve the popular patch multi-view 3D reconstruction algorithm by Furukawa and Ponce (2008). Specifically, a new method is proposed to improve the robustness by removing outliers based on a filtering approach. In addition, this work also proposes a method to divide the 3D points in to several buckets for applying the sparse bundle adjustment algorithm (SBA) individually, removing the outliers and finally merging them. The residuals are used to filter potential outliers to reduce the re-projection error used as the performance evaluation of refinement. In our experiments, the original mean re-projection error is about 47.6. After applying the proposed methods, the mean error is reduced to 2.13.     

双PTZ摄像机系统的标定

在视频监控领域,PTZ(Pan/Tilt/Zoom)摄像机因为其具有可变视角和可变焦能力,已经越来越多的被采用,而且用两个PTZ摄像机组成一个视觉系统具有可以同时获取全景图像、近景高分辨率图像以及场景深度信息等优势,虽然目前关于双PTZ视觉系统的研究还比较少,但其应用潜力很大。对双PTZ摄像机系统进行标定的目的之一是辅助两个镜头之间的配准,进而方便立体匹配等。两个摄像机之间公共坐标系的选择是标定问题种的一个关键。针对这一问题提出了一种球面校正公共坐标系,并研究了其标定问题。众所周知,传统的多目镜头标定主要针对静止镜头,而且一般是通过事先估计摄像机的架设位置或者利用标定物来计算摄像机之间的关系,但此类标定方法无法直接解决上述问题。为了更好地进行标定,在此提出了一种利用多组基础矩阵的方法进行自动估计的标定方法,该方法不需要标定物,而只与镜头架设方式有关,且不受摄像机PTZ参数改变影响。实验结果表明,该方法具有方便和实用性。     

People tracking using a network-based PTZ camera

In this paper, we propose a method for online upper body tracking using an IP PTZ camera. This type of camera uses a built-in Web server resulting in variable response times when sending control commands. Furthermore, communicating with a Web server involves network delays. Thus, because the camera is inside a control loop, the effective frame rate that can be processed by a computer vision method is irregular and in general low (2–6 fps). Our tracking method has been specifically designed to perform in such conditions. It detects, at every frame, candidate blobs using motion detection, region sampling, and region color appearance. The target is detected among candidate blobs using a fuzzy classifier. Then, a movement command is sent to the camera using the target position and speed. The proposed method can cope with low frame rate, and thus with large motion of the target, even in the case of a fast walk. Results show that our system has a good target detection precision (>88%) and low track fragmentation, and the target is almost always localized within 1/6th of the image diagonal from the image center.     

Discrete camera calibration from pixel streams

We consider the problem of estimating the relative orientation of a number of individual photocells – or pixels – that hold fixed relative positions. The photocells measure the intensity of light traveling on a pencil of lines. We assume that the light-field thus sampled is changing, e.g. as the result of motion of the sensors and use the obtained measurements to estimate the orientations of the photocells.Our approach is based on correlation and information-theory dissimilarity measures. Experiments with real-world data show that the dissimilarity measures are strongly related to the angular separation between the photocells, and the relation can be modeled quantitatively. In particular we show that this model allows to estimate the angular separation from the dissimilarity. Although the resulting estimators are not very accurate, they maintain their performance throughout different visual environments, suggesting that the model encodes a very general property of our visual world. Finally, leveraging this method to estimate angles from signal pairs, we show how distance geometry techniques allow to recover the complete sensor geometry.     

高速公路云台相机的自动标定

目的 云台相机因监控视野广、灵活度高,在高速公路监控系统中发挥出重要的作用,但因云台相机焦距与角度不定时地随监控需求变化,对利用云台相机的图像信息获取真实世界准确的物理信息造成一定困难,因此进行云台相机非现场自动标定方法的研究对高速公路监控系统的应用具有重要价值。方法 本文提出了一种基于消失点约束与车道线模型约束的云台相机自动标定方法,以建立高速公路监控系统的图像信息与真实世界物理信息之间准确描述关系。首先,利用车辆目标运动轨迹的级联霍夫变换投票实现纵向消失点的准确估计,其次以车道线模型物理度量为约束,并采用枚举策略获取横向消失点的准确估计,最终在已知相机高度的条件下实现高速公路云台相机标定参数的准确计算。结果 将本文方法在不同的场景下进行实验,得到在不同的距离下的平均误差分别为4.63%、4.74%、4.81%、4.65%,均小于5%。结论 对多组高速公路监控场景的测试实验结果表明,本文提出的云台相机自动标定方法对高速公路监控场景的物理测量误差能够满足应用需求,与参考方法相比较而言具有较大的优势和一定的应用价值,得到的相机内外参数可用于计算车辆速度与空间位置等。     

Paracatadioptric camera calibration

Catadioptric sensors refer to the combination of lens-based devices and reflective surfaces. These systems are useful because they may have a field of view which is greater than hemispherical, providing the ability to simultaneously view in any direction. Configurations which have a unique effective viewpoint are of primary interest, among these is the case where the reflective surface is a parabolic mirror and the camera is such that it induces an orthographic projection and which we call paracatadioptric. We present an algorithm for the calibration of such a device using only the images of lines in space. In fact, we show that we may obtain all of the intrinsic parameters from the images of only three lines and that this is possible without any metric information. We propose a closed-form solution for focal length, image center, and aspect ratio for skewless cameras and a polynomial root solution in the presence of skew. We also give a method for determining the orientation of a plane containing two sets of parallel lines from one uncalibrated view. Such an orientation recovery enables a rectification which is impossible to achieve in the case of a single uncalibrated view taken by a conventional camera. We study the performance of the algorithm in simulated setups and compare results on real images with an approach based on the image of the mirror's bounding circle     

CCD摄像机标定

在基于单目视觉的农业轮式移动机器人自主导航系统中,CCD摄像机标定是农业轮式移动机器人正确和安全导航的前提和关键。摄像机标定确立了地面某点的三维空间坐标与计算机图像二维坐标之间的对应关系,机器人根据该关系计算出车体位姿值自主导航。因此,根据CCD摄像机针孔成像模型,利用大地坐标系中平面模板上已知的各点坐标,建立与计算机图像空间中各对应像素值之间的关系方程组,在Matlab环境下拟合出摄像机各内外参数。实验结果表明：该方法可以正确完成CCD摄像机标定。     

Foreground segmentation with PTZ camera: a survey

Multimedia Tools and Applications - The alertness of terrorism in the present is greater than that in the past with reference to the incident of September 11. Still now, there has been a fight...     

基于交叉垂直线的摄像机标定新算法

提出了一种基于交叉垂直直线的摄像机标定算法。利用两条垂直相交的直线作为标定物,令其绕交叉点作任意角度的转动，将摄像机固定拍摄6幅以上的图片,即可运用直线的特点以及交叉垂直的约束条件建立线性方程组，求解摄像机的内参数。实验证明该算法具有较高的精度和较好的鲁棒性。     

鱼眼相机与PTZ相机相结合的主从目标监控系统

提出了一种将鱼眼相机和PTZ相机相结合的主从目标监控系统,充分利用鱼眼相机单镜头半球空间成像以及PTZ相机指向性高分辨率成像的优点,实现了单系统半球空间运动目标的高分辨率成像监控。首先采用运动点团模式实现鱼眼图像中运动目标的检测;然后在鱼眼图像空间计算目标的相对方位角P′、俯仰角T′和距离Z′;最后通过参数映射将其映射到PTZ图像空间,输出PTZ控制信号给相机进行指向性成像。PTZ图像空间中的P参数和T参数结合鱼眼镜头畸变系数进行校正计算,Z参数根据目标在鱼眼图像中的相对尺寸及PTZ图像中需要的尺寸进行计算。通过对PTZ参数的多次实验测量,其结果的误差均在系统要求范围之内。系统实际的户外测试结果表明,系统能准确检测出鱼眼图像中的运动目标,在PTZ参数的控制下,PTZ相机能准确指向目标进行二次高分辨率成像,目标在PTZ图像中的位置和大小合适,达到预期的设计目标。     

Camera calibration from images of spheres

This paper introduces a novel approach for solving the problem of camera calibration from spheres. By exploiting the relationship between the dual images of spheres and the dual image of the absolute conic (IAC), it is shown that the common pole and polar with regard to the conic images of two spheres are also the pole and polar with regard to the IAC. This provides two constraints for estimating the IAC and, hence, allows a camera to be calibrated from an image of at least three spheres. Experimental results show the feasibility of the proposed approach     

一种改进的相机标定方法

摄像机标定与图像畸变修正是摄影测量、视觉检测、计算机视觉等领域的重点研究课题之一,在测绘、工业控制、导航、军事等领域得到了极大的应用。研究了摄像机模型,摄像机标定等内容。对DLT的标定方法进行了改进,在摄像机模型中全面考虑了镜头的畸变,利用图像中心附近点畸变量较小的性质,提出一种摄像机内外部参数和像差修正参数分离的标定方法。并举例说明了基于同一物体的两幅图画三维重构的具体实验步骤和方法,分析了影响精度的因素。     

Segment Based Camera Calibration

The basic idea of calibrating a camera system in previous approaches is to determine camera parmeters by using a set of known 3D points as calibration reference.In this paper,we present a method of camera calibration in whih camera parameters are determined by a set of 3D lines.A set of constraints is derived on camea parameters in terms of perspective line mapping.Form these constraints,the same perspective transformation matrix as that for point mapping can be computed linearly.The minimum number of calibration lines is 6.This result generalizes that of Liu,Huang and Faugeras^[12] for camera location determination in which at least 8 line correspondences are required for linear computation of camera location.Since line segments in an image can be located easily and more accurately than points,the use of lines as calibration reference tends to ease the computation in inage preprocessing and to improve calibration accuracy.Experimental results on the calibration along with stereo reconstruction are reported.     

Persistent people tracking and face capture using a PTZ camera

Pan–tilt–zoom (PTZ) camera is a powerful tool in far-field scenarios. However, most of the current PTZ surveillance systems require manual intervention to move the camera to the desired position. In this paper, we address the problem of persistent people tracking and face capture in uncontrolled scenarios using a single PTZ camera, which could prove most helpful in forensic applications. The system first detects and tracks pedestrians in zoomed-out mode. Then, according to a scheduler, the system selects a person to zoom in. In the zoomed-in mode, we detect a set of face images and solve the face–face association and face–person association problems. The system then zooms back out where tracking is continued as people re-appear in the view. The person–person association module associates the people on the schedule list with the people in the current view. The detected faces are associated with the corresponding people and trajectories. Due to the dynamic nature of our problem, e.g. the field of view of the camera changes because of the pan/tilt/zoom movement of the camera, all of the processes including receiving images from the camera and processing must be done in real time. To the best of our knowledge, the proposed method is the first to address the association of face images to people and trajectories using a single PTZ camera. Extensive experiments in challenging indoor and outdoor uncontrolled conditions demonstrate the effectiveness of the proposed system.     

GPS coordinates estimation and camera calibration from solar shadows

In this paper, we discuss the issue of camera parameter estimation (intrinsic and extrinsic parameters), along with estimation of the geo-location of the camera by using only the shadow trajectories. By observing stationary objects over a period of time, it is shown that only six points on the trajectories formed by tracking the shadows of the objects are sufficient to estimate the horizon line of the ground plane. This line is used along with the extracted vertical vanishing point to calibrate the stationary camera. The method requires as few as two shadow casting objects in the scene and a set of six or more points on the shadow trajectories of these objects. Once camera intrinsic parameters are recovered, we present a novel application where one can accurately determine the geo-location of the camera up to a longitude ambiguity using only three points from these shadow trajectories without using any GPS or other special instruments. We consider possible cases where this ambiguity can also be removed if additional information is available. Our method does not require any knowledge of the date or the time when the images are taken, and recovers the date of acquisition directly from the images. We demonstrate the accuracy of our technique for both steps of calibration and geo-temporal localization using synthetic and real data.     

Automatic detection of pedestrians from stereo camera images

We propose a technique for detecting pedestrians by employing stereo camera images and based on probabilistic voting. From a disparity map, each pixel on the image is voted on a depth map employing a 2-D Gaussian distribution. The region having the peak value in the vote is chosen as the foot of an object. The object is specified by a rectangle on the right image, which is referred to as the region of interest (ROI). This ROI is described by HOG features, and is judged by SVM if it contains a person. With an ROI containing a person, a Kalman filter is applied to track the person through successive image frames. The performance of the detection of people was evaluated by employing ground truth data. The ratio of people detected to the ground truth data, called the recall rate, was 80%. This is a satisfactory result.     

Character location in scene images from digital camera

In this paper, a robust, connected-component-based character locating method is presented. It is an important part of an optical character recognition (OCR) system. Color clustering is used to separate the color image into homogeneous color layers. Next, for each color layer, every connected component in color layers is analyzed using black adjacency graph (BAG), and the component-bounding box is computed. Then, for coarse detection of characters, an aligning-and-merging-analysis (AMA) scheme is proposed to locate all the potential characters using the information about the bounding boxes of connected components in all color layers. Finally, to eliminate false characters, a four-step identification of characters is used. The experimental results in this paper have proven that the method is effective.