Simple Camera Calibration Method Research Articles

A simplified nonlinear regression method for human height estimation in video surveillance

This paper presents a simple camera calibration method for estimating human height in video surveillance. Given that most cameras for video surveillance are installed in high positions at a slightly tilted angle, it is possible to retain only three calibration parameters in the original camera model, namely the focal length, the tilting angle and the camera height. These parameters can be directly estimated using a nonlinear regression model from the observed head and foot points of a walking human instead of estimating the vanishing line and point in the image, which is extremely sensitive to noise in practice. With only three unknown parameters, the nonlinear regression model can fit data efficiently. The experimental results show that the proposed method can predict the human height with a mean absolute error of only about 1.39 cm from ground truth data.

Camera calibration by multiplexed phase encoding of coordinate information.

A simple camera calibration method based on the principle of phase encoding and coordinate transformation is proposed. We use a reference coordinate frame encoded as a phase distribution by multiplexing the x and y directions. From this, we suggest a phase demodulation system. The coordinate transformation induced by the imaging is exploited to estimate the intrinsic and extrinsic camera parameters by using the least-squares method. Thus, a robust and noniterative estimation scheme is obtained. Simulations and experimental results show the feasibility of the proposal. Because of the potential for calibrating projectors, the proposed method could be used to calibrate fringe-projection systems.

Keeping smart, omnidirectional eyes on you

An adaptive panoramic stereo approach for two cooperative mobile platform is presented. There are four key features in the approach: 1) omnidirectional stereovision with an appropriate vertical FOV, and a simple camera calibration method; 2) cooperative mobile platforms for mutual dynamic calibration and best view planning; 3) 3D matching after meaningful object (human subject) extraction; and 4) real-time performance. The integration of omnidirectional vision with mutual awareness and dynamic calibration strategies allows intelligent cooperation between visual agents. This provides an effective way to solve the problems of limited resources, view planning, occlusion, and motion detection of movable robotic platforms. Experiments have shown that this approach is quite promising.

Camera calibration for a mobile robot prototype

Stereovision is an effective technique to use a CCD video camera to determine the 3D position of a target object from two or more simultaneous views of the scene. Camera calibration is a central issue in finding the position of objects in a stereovision system. This is usually carried out by calibrating each camera independently, and then applying a geometric transformation of the external parameters to find the geometry of the stereo setting. After calibration, the distance of various target objects in the scene can be calculated with CCD video cameras, and recovering the 3D structure from 2D images becomes simpler. However, the process of camera calibration is complicated. Based on the ideal pinhole model of a camera, we describe formulas to calculate intrinsic parameters that specify the correct camera characteristics, and extrinsic parameters that describe the spatial relationship between the camera and the world coordinate system. A simple camera calibration method for our CCD video cameras and corresponding experiment results are also given.

New algorithm for recognizing cylindrical patterns by surface backprojection

In this paper, a simple and fast approach is proposed to recognize the unknown cylindrical patterns using backprojection techniques. The method uses a known-height label pasted on the cylindrical object, on which an object pattern (so-called cylindrical patterns) is associated for the robot to identify the desired object. First, a simple camera calibration method is used to compute the camera parameters by a single camera view of the object. A surface backprojection technique is then adopted to reconstruct the pattern on the cylindrical surface of the object. Finally, the reconstructed pattern is matched with those of each object model using the difference of their NRD spectrum. Experimental results to prove the feasibility of the proposed method are also included.