• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.8
• /
• pp.309-314
• /
• 2014

To generate a complete 3D model from depth images of multiple RGB-D cameras, it is necessary to find 3D transformations between RGB-D cameras. This paper proposes a convenient view calibration technique using a spherical object. Conventional view calibration methods use either planar checkerboards or 3D objects with coded-pattern. In these conventional methods, detection and matching of pattern features and codes takes a significant time. In this paper, we propose a convenient view calibration method using both 3D depth and 2D texture images of a spherical object simultaneously. First, while moving the spherical object freely in the modeling space, depth and texture images of the object are acquired from all RGB-D camera simultaneously. Then, the external parameters of each RGB-D camera is calibrated so that the coordinates of the sphere center coincide in the world coordinate system.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.6
• /
• pp.125-132
• /
• 2008

Omnidirectional camera system with a wide view angle is widely used in surveillance and robotics areas. In general, most of previous studies on estimating a projection model and the extrinsic parameters from the omnidirectional images assume corresponding points previously established among views. This paper presents a novel omnidirectional camera calibration based on automatic contour matching. In the first place, we estimate the initial parameters including translation and rotations by using the epipolar constraint from the matched feature points. After choosing the interested points adjacent to more than two contours, we establish a precise correspondence among the connected contours by using the initial parameters and the active matching windows. The extrinsic parameters of the omnidirectional camera are estimated minimizing the angular errors of the epipolar plane of endpoints and the inverse projected 3D vectors. Experimental results on synthetic and real images demonstrate that the proposed algorithm obtains more precise camera parameters than the previous method.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.5
• /
• pp.101-107
• /
• 2009

In this paper, we describe a method of detecting emergency situation using images from surveillance cameras and propose a mobile robot tracking system for detailed examination of that situation. We are able to track a few persons and recognize their actions by an analyzing image sequences acquired from a fixed camera on all sides of buildings. When emergency situation is detected, a mobile robot moves and closely examines the place where the emergency is occurred. In order to recognize actions of a few persons using a sequence of images from surveillance cameras images, we need to track and manage a list of the regions which are regarded as human appearances. Interest regions are segmented from the background using MOG(Mixture of Gaussian) model and continuously tracked using appearance model in a single image. Then we construct a MHI(Motion History Image) for a tracked person using silhouette information of region blobs and model actions. Emergency situation is finally detected by applying these information to neural network. And we also implement mobile robot tracking technology using the distance between the person and a mobile robot.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.46 no.1
• /
• pp.128-135
• /
• 2009

Techniques for tracking the same region of physical space with the temporal sequences of images by matching the contours of constant phase show robust and stable performance in relative to the tracking techniques using or assuming the constant intensity. Using this property, we describe an algorithm for obtaining the robust motion parameters caused by the global camera motion. First, we obtain the optical flow based on the phase of spacially filtered sequential images on the region in a direction orthogonal to orientation of each component of gabor filter bank. And then, we apply the least squares method to the optical flow to determine the affine motion parameters. We demonstrate hat proposed method can be applied to the vision based pointing device which estimate its motion using the image including the display device which cause lighting condition varieties and noise.

• Journal of Korea Multimedia Society
• /
• v.5 no.2
• /
• pp.167-175
• /
• 2002

Image Mosaicing creates a new image by composing video frames or still images that are related, and performed by arrangement, composition and redundancy analysis of images. This paper proposes a hierarchical image mosaicing system using camera and object parameters far efficient video database construction. A tree-based image mosiacing has implemented for high-speed computation time and for construction of static and dynamic image mosaic. Camera parameters are measured by using least sum of squared difference and affine model. Dynamic object detection algorithm has proposed for extracting dynamic objects. For object extraction, difference image, macro block, region splitting and 4-split detection methods are proposed and used. Also, a dynamic positioning method is used for presenting dynamic objects and a blurring method is used for creating flexible mosaic image.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.11
• /
• pp.5385-5391
• /
• 2013

As on-line purchases is activated, customers' demand increases for the realistic and accurate digital information of a product design. In this paper, we propose a practical method that can generate a realistic 3D model of a real product using a 3D geometry obtained by a 3D scanner and its photographic images. In order to register images to the 3D geometry, the camera focal length, the CCD scanning aspect ratio and the transformation matrix between the camera coordinate and the 3D object coordinate must be determined. To perform this 2D-3D registration with consideration of computational complexity, a three-step method is applied, which consists of camera calibration, determination of a temporary optimum translation vector (TOTV) and nonlinear optimization for three rotational angles. A case study for a metallic coated industrial part, of which the colour appearance is hardly obtained by a 3D colour scanner has performed to demonstrate the effectiveness of the proposed method.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.3
• /
• pp.55-62
• /
• 2013

This paper aims to detect Speed Bump by using Omni-directional Camera and to suggest Real-time update scheme of Speed Bump through Vision Based Approach. In order to detect Speed Bump from sequence of camera images, noise should be removed as well as spot estimated as shape and pattern for speed bump should be detected first. Now that speed bump has a regular form of white and yellow area, we extracted speed bump on the road by applying erosion and dilation morphological operations and by using the HSV color model. By collecting huge panoramic images from the camera, we are able to detect the target object and to calculate the distance through GPS log data. Last but not least, we evaluated accuracy of obtained result and detection algorithm by implementing SLAMS (Simultaneous Localization and Mapping system).

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.31 no.4
• /
• pp.207-213
• /
• 2018

This paper investigates applicability of Microsoft $Kinect^{(R)}$, RGB-depth camera, to implement a 3D image and spatial information for sensing a target. The relationship between the image of the Kinect camera and the pixel coordinate system is formulated. The calibration of the camera provides the depth and RGB information of the target. The intrinsic parameters are calculated through a checker board experiment and focal length, principal point, and distortion coefficient are obtained. The extrinsic parameters regarding the relationship between the two Kinect cameras consist of rotational matrix and translational vector. The spatial images of 2D projection space are converted to a 3D images, resulting on spatial information on the basis of the depth and RGB information. The measurement is verified through comparison with the length and location of the 2D images of the target structure.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.2
• /
• pp.1426-1433
• /
• 2015

CCTV-applied monitoring is an effective measure to suppress potential crimes and record objective relationship; however, there is no methodology that can quantitatively compare and assess the afore-mentioned effects. Thus, this study intended to construct the methodology and analysis application that can measure the changes in the space-corresponding performance of CCTVs depending on installation measures by using 3-dimenstional virtual simulation technology. For analysis, the raster-based Isovist theory was 3-dimensionally expanded and the amount of incident sight line to each point was accumulated. At the same time, the amount of overlapped monitoring in the CCTV cameras that were connected to each measurement node was accumulated for cross-analysis. By applying the examples and analyzing the results, it was possible to construct an analysis application in use of collision detection model and quantify the changes of monitoring performance depending on positioning alternative of the cameras. Moreover, it enabled intuitive review and supplementation by reproducing visible shadow areas in a graph.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.9
• /
• pp.1060-1069
• /
• 2019

Pedestrian-based camera self-calibration methods are suitable for video surveillance systems since they do not require complex calibration devices or procedures. However, using arbitrary pedestrians as calibration targets may result in poor calibration accuracy due to the unknown height of each pedestrian. To solve this problem in the real surveillance environments, this paper proposes a novel Bayesian approach. By assuming known statistics on the height of pedestrians, we construct a probabilistic model that takes into account uncertainties in both the foot/head locations and the pedestrian heights, using foot-head homology. Since solving the model directly is infeasible, we use variational Bayesian inference, an approximate inference algorithm. Accordingly, this makes it possible to estimate the height of pedestrians and to obtain accurate camera parameters simultaneously. Experimental results show that the proposed algorithm is robust to noise and provides accurate confidence in the calibration.