• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.150-157
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• 2009

In this paper, we propose a person tracking based multi-channel audio panning system for multi-view broadcasting services. Multi-view broadcasting is to render the video sequences that are captured from a set of cameras based on different viewpoints, and multi-channel audio panning techniques are necessary for audio rendering in these services. In order to apply such a realistic audio technique to this multi-view broadcasting service, person tracking techniques which are to estimate the position of users are also necessary. For these reasons, proposed methods are composed of two parts. The first part is a person tracking method by using ultrasonic satellites and receiver. We could obtain user's coordinates of high resolution and short duration about 10 mm and 150 ms. The second part is MPEG Surround parameter-based multi-channel audio panning method. It is a method to obtain panned multi-channel audio by controlling the MPEG Surround spatial parameters. A MUSHRA test is conducted to objectively evaluate the perceptual quality and measure localization performance using a dummy head. From the experiments, it is shown that the proposed method provides better perceptual quality and localization performance than the conventional parameter-based audio panning method. In addition, we implement the prototype of person tracking based multi-view broadcasting system by integrating proposed methods with multi-view display system.

• Smart Media Journal
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• v.1 no.1
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• pp.10-16
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• 2012

Stereo and multi-view cameras have been used to capture the three-dimensional (3D) scene for 3D contents generation. Besides, depth sensors are frequently used to obtain 3D information of the captured scene in real time. In order to generate 3D contents from captured images, we need several preprocessing operations to reduce noises and distortions in the images. 3D contents are considered as the basic media for realistic broadcasting that provides photo-realistic and immersive feeling to users. In this paper, we show technical trends of 3D image capturing and contents generation, and explain some core techniques for 3D image processing for realistic 3DTV broadcasting.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.70-75
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• 1993

We represent a method of 3-D object recognition using multi images in this paper. The recognition process is executed as follows. Object models as prior knowledgement are generated and stored on a computer. To extract features of a recognized object, three CCD cameras are set at vertices of a regular triangle and take images of an object to be recognized. By comparing extracted features with generated models, the object is recognized. In general, it is difficult to recognize 3-D objects because there are the following problems such as how to make the correspondence to both stereo images, generate and store an object model according to a recognition process, and effectively collate information gotten from input images. We resolve these problems using the method that the collation on the basis of features independent on the viewpoint, the generation of object models as enumerating some candidate models in an early recognition level, the execution a tight cooperative process among results gained by analyzing each image. We have made experiments based on real images in which polyhedral objects are used as objects to be recognized. Some of results reveal the usefulness of the proposed method.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.614-623
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• 2011

We propose a real-time tracking algorithm for an augmented reality (AR) system for TV broadcasting. The tracking is initialized by detecting the object with the SURF algorithm. A multi-scale approach is used for the stable real-time camera tracking. Normalized cross correlation (NCC) is used to find the patch correspondences, to cope with the unknown and changing lighting condition. Since a zooming camera is used, the focal length should be estimated online. Experimental results show that the focal length of the camera is properly estimated with the proposed online calibration procedure.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1160-1165
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• 2011

This study is focused on the structural dynamic responses, i.e., vibration analysis results of the high-accuracy observation multi-axial camera, which is installed and operated for the UAV (Unmanned Aerial Vehicle) and helicopter etc. And, the authors newly suggest a modeling technology of the ball bearing applied to the camera by using three-axis spring elements. The vibration analysis results well agreed to the randum vibration test results. Also, the vibration responses characteristics of the multi-axial camera through the time history analysis of the random vibration were analyzed and evaluated. The above results can be applied to the FE-modeling of the ball bearings used for the space cameras.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.112-122
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• 2020

In 3D computer graphics, a depth map is an image that provides information related to the distance from the viewpoint to the subject's surface. Stereo sensors, depth cameras, and imaging systems using an active illumination system and a time-resolved detector can perform accurate depth measurements with their own light sources. The 3D image information obtained through the depth map is useful in 3D modeling, autonomous vehicle navigation, object recognition and remote gesture detection, resolution-enhanced medical images, aviation and defense technology, and robotics. In addition, the depth map information is important data used for extracting and restoring multi-view images, and extracting phase information required for digital hologram synthesis. This study is oriented toward a recent research trend in deep learning-based 3D data analysis methods and depth map information extraction technology using a convolutional neural network. Further, the study focuses on 3D image processing technology related to digital hologram and multi-view image extraction/reconstruction, which are becoming more popular as the computing power of hardware rapidly increases.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.9-17
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• 2017

Recent development in virtual and augmented reality increases the demand for content in many different fields. One of the fast ways to create content for VR is 3D modeling of real objects. In this paper we propose a system to reconstruct three-dimensional models of real objects from the set of two-dimensional images under the assumption that the subject does not has distinct features. We explicitly consider an object that is made of one or more surfaces and radiant constant energy isotropically. We design a low cost portable multi camera rig system that is capable of capturing images simultaneously from all cameras. In order to evaluate the performance of the proposed system, comparison is made between 3D model and a CAD model. A simple algorithm is also proposed to acquire original texture or color of the subject. Using best pattern found after the experiments, 3D model of the Pyeongchang Olympic Mascot "Soohorang" is created to use as VR content.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.487-493
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• 1998

Camera calibration is the process of determining the coordinate relationship between a camera image and its real world space. Accurate calibration of a camera is necessary for the applications that involve quantitative measurement of camera images. However, if the camera plane is parallel or near parallel to the calibration board on which 2 dimensional objects are defined(this is called "ill-conditioned"), existing solution procedures are not well applied. In this paper, we propose a neural network-based approach to camera calibration for 2D images formed by a mono-camera or a pair of cameras. Multi-layer perceptrons are developed to transform the coordinates of each image point to the world coordinates. The validity of the approach is tested with data points which cover the whole 2D space concerned. Experimental results for both mono-camera and stereo-camera cases indicate that the proposed approach is comparable to Tsai's method［8］. Especially for the stereo camera case, the approach works better than the Tsai's method as the angle between the camera optical axis and the Z-axis increases. Therefore, we believe the approach could be an alternative solution procedure for the ill -conditioned camera calibration.libration.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.631-632
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• 2006

The assembly of camera lens modules fur the mobile phone has not been automated so far. They are still assembled manually because of high precision of all parts and hard-to-recognize lens by vision camera. In addition, the very short life cycle of the camera phone lens requires flexible and intelligent automation. This study proposes a fast and accurate identification system of the parts by distributing the camera for 4 degree of freedom assembly robot system. Single or multi-cameras can be installed according to the part's image capture and processing mode. It has an agile structure which enables adaptation with the minimal job change. The framework is proposed and the experimental result is shown to prove the effectiveness.

• Smart Media Journal
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• v.11 no.8
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• pp.84-92
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• 2022

Precision livestock monitoring promises greater management efficiency for farmers and higher welfare standards for animals. Recent studies on video-based animal activity recognition and tracking have shown promising solutions for understanding animal behavior. To achieve that, surveillance cameras are installed diagonally above the barn in a typical cattle farm setup to monitor animals constantly. Under these circumstances, tracking individuals requires addressing challenges such as occlusion and visual appearance, which are the main reasons for track breakage and increased misidentification of animals. This paper presents a framework for multi-cattle tracking in closed barns with appearance and motion models. To overcome the above challenges, we modify the DeepSORT algorithm to achieve higher tracking accuracy by three contributions. First, we reduce the weight of appearance information. Second, we use an Ensemble Kalman Filter to predict the random motion information of cattle. Third, we propose a supplementary matching algorithm that compares the absolute cattle position in the barn to reassign lost tracks. The main idea of the matching algorithm assumes that the number of cattle is fixed in the barn, so the edge of the barn is where new trajectories are most likely to emerge. Experimental results are performed on our dataset collected on two cattle farms. Our algorithm achieves 70.37%, 77.39%, and 81.74% performance on HOTA, AssA, and IDF1, representing an improvement of 1.53%, 4.17%, and 0.96%, respectively, compared to the original method.