• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.71-75
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• 2021

The article discusses the dead reckoning of the traveled path based on the analysis of the video data stream coming from the optoelectronic surveillance devices; the use of relief data makes it possible to partially compensate for the shortcomings of the first method. Using the overlap of the photo-video data stream, the terrain is restored. Comparison with a digital terrain model allows the location of the aircraft to be determined; the use of digital images of the terrain also allows you to determine the coordinates of the location and orientation by comparing the current view information. This method provides high accuracy in determining the absolute coordinates even in the absence of relief. It also allows you to find the absolute position of the camera, even when its approximate coordinates are not known at all.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.26-34
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• 1999

This study shows an alternative method for the determination of object's position, based on a computer vision method. This approach develops the vision system model to define the reciprocal relationship between the 3-D real space and 2-D image plane. The developed model involves the bilinear six-view parameters, which is estimated using the relationship between the camera space location and real coordinates of known position. Based on estimated parameters in independent cameras, the position of unknown object is accomplished using a sequential estimation scheme that permits data of unknown points in each of the 2-D image plane of cameras. This vision control methods the robust and reliable, which overcomes the difficulties of the conventional research such as precise calibration of the vision sensor, exact kinematic modeling of the robot, and correct knowledge of the relative positions and orientation of the robot and CCD camera. Finally, the developed vision control method is tested experimentally by performing determination of object position in the space using computer vision system. These results show the presented method is precise and compatible.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.135-140
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• 2007

Due to the expensiveness of IKONOS Pro and Precision Products, it is attractive to use the low-cost IKONOS Geo Product with vendor-provided RPCs to produce highly accurate mapping products. The imaging geometry of IKONOS high-resolution imagery is described by RFs instead of rigorous sensor models. This paper presents four different models defined respectively in object space and image space to improve the accuracies of the RF-derived ground coordinates. The four models include the offset model, the scale & offset model, the affine model and the 2nd-order polynomial model. Different configurations of ground control points (GCPs) are carefully examined to evaluate the effect of the GCPs arrangement on the accuracy of ground coordinates. The experiment also evaluates the effect of different cartographic parameters such as the number location, and accuracy of GCPs on the accuracy of geopositioning.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.12
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• pp.1223-1231
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• 2009

We derive a systematic and iterative calibration algorithm, and position and pose estimation algorithm for the mobile robots in formation system based on the vision system. In addition, we develop a coordinate matching algorithm which calculates matched sequence of order in both extracted image coordinates and object coordinates for non interactive calibration and pose estimation. Based on the results of calibration, we also develop a camera simulator to confirm the results of calibration and compare the results of simulations with those of experiments in position and pose estimation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.15 no.2
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• pp.185-193
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• 1997

While close range photogrammetry has been widely applied for static deformation analysis, video cameras have many characteristics that make them the sensors of choice for dynamic analysis of rapidly changing situations. They also have limitations. The aim of this research is to explore the potential of a video system for monitoring dynamic objects. A pilot system consists of two camcorders, VCR, and PC with frame grabber. To estimte the performance of this system for moving objects, a car was imaged covering several phases when starting to drive. The sequential images of a moving car were recorded on VCR. 15 images per second were digitized in an off-line mode by frame grabber. The image coordinates of targets attached to the rear bumper of a car were acquired by IDRISI, and the object coordinates were derived based on DLT. This research suggests that home video cameras, PC, and photogrammetric principles are promising tools for monitoring of the moving objects and vibrations as well as other time dependent situations.

• Journal of the Ergonomics Society of Korea
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• v.7 no.1
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• pp.31-37
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• 1988

This paper designs, in the teleoperation task, the world coordinate system by the functional analysis of each of the robot joint so that the human operator performs easily the task. Also, it constructs the heuristic rules of the equal motion line coordinates for the position and the posture control of the robot within the knowledge base so that the robot hand reaches-possibly in any position of the robot's work space. As shown in the result of the experiments. the coordinate reading is easy because the work station is displayed to the high resolution by using the superimposer of the motion analysing computer system. Also. the task burden of the human operator reduces and the error recovery time reduces because the coordinates of the object is obtained just by touch using the digitizer.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.61-72
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• 2020

Recently, researches that are using deep learning technology in various fields are being conducted. The fields include geographic map processing. In this paper, I propose a method to infer where the map area included in the image is. The proposed method generates and learns images including a map, detects map areas from input images, extracts character strings belonging to those map areas, and converts the extracted character strings into coordinates through geocoding to infer the coordinates of the input image. Faster R-CNN was used for learning and map detection. In the experiment, the difference between the center coordinate of the map on the test image and the center coordinate of the detected map is calculated. The median value of the results of the experiment is 0.00158 for longitude and 0.00090 for latitude. In terms of distance, the difference is 141m in the east-west direction and 100m in the north-south direction.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.252-259
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• 2017

Nowadays, the existing manual method for recording actual progress of the construction site has some drawbacks, such as great reliance on the experience of professional engineers, work-intensive, time consuming and error prone. A method integrating computer vision and BIM(Building Information Modeling) is presented for indoor automatic progress monitoring. The developed method can accurately calculate the engineering quantity of target component in the time-lapse images. Firstly, sample images of on-site target are collected for training the classifier. After the construction images are identified by edge detection and classifier, a voting algorithm based on mathematical geometry and vector operation will divide the target contour. Then, according to the camera calibration principle, the image pixel coordinates are conversed into the real world Coordinate and the real coordinates would be corrected with the help of the geometric information in BIM model. Finally, the actual engineering quantity is calculated.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.93-101
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• 2023

In this paper, we propose a method of creating a 3D bounding box for an object using a vanishing point to increase the accuracy of object recognition in an image when recognizing an traffic object using a video camera. Recently, when vehicles captured by a traffic video camera is to be detected using artificial intelligence, this 3D bounding box generation algorithm is applied. The vertical vanishing point (VP1) and horizontal vanishing point (VP2) are derived by analyzing the camera installation angle and the direction of the image captured by the camera, and based on this, the moving object in the video subject to analysis is specified. If this algorithm is applied, it is easy to detect object information such as the location, type, and size of the detected object, and when applied to a moving type such as a car, it is tracked to determine the location, coordinates, movement speed, and direction of each object by tracking it. Able to know. As a result of application to actual roads, tracking improved by 10%, in particular, the recognition rate and tracking of shaded areas (extremely small vehicle parts hidden by large cars) improved by 100%, and traffic data analysis accuracy was improved.

• Journal of Broadcast Engineering
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• v.7 no.3
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• pp.248-261
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• 2002

In this paper, we proposed a new 3-dimensional(3-D) object-tracking algorithm that can control a stereo camera using a variable window mask supported by which uses ,B-D information and an optical BPEJTC. Hence, three-dimensional information characteristics of a stereo vision system, distance information from the stereo camera to the tracking object. can be easily acquired through the elements of a stereo vision system. and with this information, we can extract an area of the tracking object by varying window masks. This extractive area of the tracking object is used as the next updated reference image. furthermore, by carrying out an optical BPEJTC between a reference image and a stereo input image the coordinates of the tracking objects location can be acquired, and with this value a 3-D object tracking can be accomplished through manipulation of the convergence angie and a pan/tilt of a stereo camera. From the experimental results, the proposed algorithm was found to be able to the execute 3-D object tracking by extracting the area of the target object from an input image that is independent of the background noise in the stereo input image. Moreover a possible implementation of a 3-D tele-working or an adaptive 3-D object tracker, using the proposed algorithm is suggested.