• Electronics and Telecommunications Trends
• /
• v.20 no.4 s.94
• /
• pp.16-28
• /
• 2005

최근 컴퓨터비전 기술을 이용하는 새로운 패러다임의 마커프리 모션캡처 기술이 미국의 MIT, CMU, MS, 일본의 ATR, MERL, 영국의 Oxford 대학 등에서 개발되고 있다. 마커프리 모션캡처는 연기자의 몸에 마커나 센서를 부착하지 않으며 특별한 조명이 필요 없으므로, 애니메이션 제작뿐만 아니라 일반인을 대상으로 하는 동작 인터페이스분야로의 확대 적용이 가능한 모션캡처 방식이다. ETRI에서는 여러 응용 분야에 모션인터페이스로 활용할 수 있는 환경 변화에 강인한 마커프리 모션캡처 시스템을 개발하고 있다. 몸에 마커나 센서를 부착하지 않은 자유 복장 상태의 동작자에 대해 조명 조건 변화 및 배경 변화에 강건하게 실시간 모션캡처 할 수 있는 기술 개발을 목표로 한다. 본 연구 개발이 성공한다면, 2007년에 876억 달러 규모로 확대될 전망인 영화, 방송물, 게임 등을 포함한 세계 영상 콘텐츠 시장에서 핵심 요소 기술 역할을 할 것이다. 그리고, 차세대 3D OS에서는 직관적 3D 포인팅 수단으로 활용될 수 있을 것이며, 2004년에 18,600만 대가 출고된 PC 시장을 고려하면 폭발적 수요가 예측된다.

• Korean Journal of Cognitive Science
• /
• v.18 no.2
• /
• pp.139-157
• /
• 2007

We compared brain activation areas during participants drawn from contrasting two college majors performed intentionality detection (known as the basic mechanism of theory of mind) task using fMRI. The main purpose of this study was to identify whether individual differences are present in intentionality detection or not. In psychology major, the left inferior frontal gyrus, the fusiform gyrus, the superior temporal gyrus and the right fusiform gyrus, the supramarginal gyrus were activated. In engineering major, the inferior parietal lobule and the superior parietal lobule were found. This result suggests that according to participants' major, different brain areas were activated. The relations between performance of the intentionality detection task and the individual variants of participants were discussed.

• Journal of the Korea Computer Industry Society
• /
• v.3 no.9
• /
• pp.1145-1156
• /
• 2002

A method to measure the speed and distance of moving object is proposed using the stereo vision system. One of the most important factors for measuring the speed and distance of moving object is the accuracy of object tracking. Accordingly, the background image algorithm is adopted to track the rapidly moving object and the local opening operator algorithm is used to remove the shadow and noise of object. The extraction efficiency of moving object is improved by using the adaptive threshold algorithm independent to variation of brightness. Since the left and right central points are compensated, the more exact speed and distance of object can be measured. Using the background image algorithm and local opening operator algorithm, the computational processes are reduced and it is possible to achieve the real-time processing of the speed and distance of moving object. The simulation results show that background image algorithm can track the moving object more rapidly than any other algorithm. The application of adaptive threshold algorithm improved the extraction efficiency of the target by reducing the candidate areas. Since the central point of the target is compensated by using the binocular parallax, the error of measurement for the speed and distance of moving object is reduced. The error rate of measurement for the distance from the stereo camera to moving object and for the speed of moving object are 2.68% and 3.32%, respectively.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.2
• /
• pp.23-29
• /
• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.

• The KIPS Transactions:PartB
• /
• v.9B no.6
• /
• pp.853-862
• /
• 2002

Face Detection can be defined as follows : Given a digitalized arbitrary or image sequence, the goal of face detection is to determine whether or not there is any human face in the image, and if present, return its location, direction, size, and so on. This technique is based on many applications such face recognition facial expression, head gesture and so on, and is one of important qualify factors. But face in an given image is considerably difficult because facial expression, pose, facial size, light conditions and so on change the overall appearance of faces, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact face detection which overcomes some restrictions by using neural network. The proposed system can be face detection irrelevant to facial expression, background and pose rapidily. For this. face detection is performed by neural network and detection response time is shortened by reducing search region and decreasing calculation time of neural network. Reduced search region is accomplished by using skin color segment and frame difference. And neural network calculation time is decreased by reducing input vector sire of neural network. Principle Component Analysis (PCA) can reduce the dimension of data. Also, pose estimates in extracted facial image and eye region is located. This result enables to us more informations about face. The experiment measured success rate and process time using the Squared Mahalanobis distance. Both of still images and sequence images was experimented and in case of skin color segment, the result shows different success rate whether or not camera setting. Pose estimation experiments was carried out under same conditions and existence or nonexistence glasses shows different result in eye region detection. The experiment results show satisfactory detection rate and process time for real time system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.8
• /
• pp.1352-1357
• /
• 2002

This Paper describes algorithm to extract license plates in vehicle images. Conventional methods perform preprocessing on the entire vehicle image to produce the edge image and binarize it. Hough transform is applied to the binary image to find horizontal and vertical lines, and the license plate area is extracted using the characteristics of license plates. Problems with this approach are that real-time processing is not feasible due to long processing time and that the license plate area is not extracted when lighting is irregular such as at night or when the plate boundary does not show up in the image. This research uses the gray level transition characteristics of license plates to verify the digit area by examining the digit width and the level difference between the background area the digit area, and then extracts the plate area by testing the distance between the verified digits. This research solves the problem of failure in extracting the license plates due to degraded plate boundary as in the conventional methods and resolves the problem of the time requirement by processing the real time such that practical application is possible. This paper Presents a power automated license plate recognition system, which is able to read license numbers of cars, even under circumstances, which are far from ideal. In a real-life test, the percentage of rejected plates wan 13%, whereas 0.4% of the plates were misclassified. Suggestions for further improvements are given.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.45 no.3
• /
• pp.12-19
• /
• 2008

Detection of a number plate consists of three stages; division of a number plate, extraction of each character from the plate, recognition of the characters. Among of these three states, division stage of a number plate is the most important part and also the most time-consuming state. This paper suggests an effective region extraction method of a number plate for various images obtained from unmanned inspection systems of illegal parking violation, especially when we have to consider the diverse surrounding environments of roads. Our approaching method detects each region by investigating the characteristics in changes of brightness and intensity between the background part and character part, and the characteristics on character parts such as the sizes, heights, widths, and distance in between two characters. The method also divides a number plate into different types of the plate. This research can solve the number plate region detection failure problems caused by plate edge damages not only for Korean domestic number plates but also for new European style number plates. The method also reduces the time consumption by processing the detection in real-time, therefore, it can be used as a practical solution.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.2 s.314
• /
• pp.116-125
• /
• 2007

In this paper, we propose a illumination environment adaptive real-time surveillance system for security of important area such as military bases, prisons, and strategic infra structures. The proposed system recognizes movement of objects on the bright environments as well as in dark illumination. The procedure of proposed system may be summarized as follows. First, the system discriminates between bright and dark with input image distribution. Then, if the input image is dark, the system has a pre-processing. The Multi-scale Retinex Color Restoration(MSRCR) is processed to enhance the contrast of image captured in dark environments. Secondly, the enhanced input image is subtracted with the revised background image. And then, we take a morphology image processing to obtain objects correctly. Finally, each bounding box enclosing each objects are tracked. The center point of each bounding box obtained by the proposed algorithm provides more accurate tracking information. Experimental results show that the proposed system provides good performance even though an object moves very fast and the background is quite dark.

• Journal of Broadcast Engineering
• /
• v.19 no.5
• /
• pp.640-655
• /
• 2014

Many of today's video systems have additional depth camera to provide extra features such as 3D support. Thanks to these changes made in multimedia system, it is now much easier to obtain depth information of the video. Depth information can be used in various areas such as object classification, background area recognition, and so on. With depth information, we can achieve even higher coding efficiency compared to only using conventional method. Thus, in this paper, we propose the 2D video coding algorithm which uses depth information on top of the next generation 2D video codec HEVC. Background area can be recognized with depth information and by performing HEVC with it, coding complexity can be reduced. If current CU is background area, we propose the following three methods, 1) Earlier stop split structure of CU with PU SKIP mode, 2) Limiting split structure of CU with CU information in temporal position, 3) Limiting the range of motion searching. We implement our proposal using HEVC HM 12.0 reference software. With these methods results shows that encoding complexity is reduced more than 40% with only 0.5% BD-Bitrate loss. Especially, in case of video acquired through the Kinect developed by Microsoft Corp., encoding complexity is reduced by max 53% without a loss of quality. So, it is expected that these techniques can apply real-time online communication, mobile or handheld video service and so on.

• The Journal of the Korea Contents Association
• /
• v.18 no.2
• /
• pp.1-10
• /
• 2018

Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. Recently, some studies have applied information technology to a livestock management system to minimize the damage resulting from such anomalies. Nonetheless, detecting each pig in a crowed pigsty is still challenging problem. In this paper, we propose a new Kinect camera and deep learning-based monitoring system for the detection of the individual pigs. The proposed system is characterized as follows. 1) The background subtraction method and depth-threshold are used to detect only standing-pigs in the Kinect-depth image. 2) The standing-pigs are detected by using YOLO (You Only Look Once) which is the fastest and most accurate model in deep learning algorithms. Our experimental results show that this method is effective for detecting individual pigs in real time in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (average 99.40% detection accuracies).