• Transportation Technology and Policy
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• v.10 no.3
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• pp.56-62
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• 2013

• Annual Conference of KIPS
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• 2010.04a
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• pp.389-392
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• 2010

기존에 없던 열화상 카메라의 기능에서 온도가 일정 수준 이상인 지점을 여러 지점 표시하는 알고리즘을 추가하였다. 또한 산업 현장뿐만 아니라 이번에 발생한 신종 플루도 감지하기 위해서 열을 통해 사람을 구분하는 방법을 추가하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.3
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• pp.60-68
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• 2007

In this paper, we propose an automatic facial expressions recognition system to analyze facial expressions (happiness, disgust, surprise and neutral) using tree structures based on heuristic rules. The facial region is first obtained using skin-color model and connected-component analysis (CCs). Thereafter the origins of user's eyes are localized using neural network (NN)-based texture classifier, then the facial features using some heuristics are localized. After detection of facial features, the facial expression recognition are performed using decision tree. To assess the validity of the proposed system, we tested the proposed system using 180 facial image in the MMI, JAFFE, VAK DB. The results show that our system have the accuracy of 93%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

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

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.127-128
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• 2020

다양한 장치와 상황을 인식하여 사람에게 필요한 장치를 제어하는 기술 중 사람의 모션 인식을 활용한 응용과 서비스가 증가하고 있다. 이러한 기술들은 카메라를 이용하여 사람의 모션을 캡처하는 방식과 센서를 이용하여 상황을 유추하는 방식이 대표적이다. 그러나 사람의 모션을 인식하는 기존의 방식들은 큰 단위 움직임을 추적하기 때문에 드론제어와 같이 섬세하면서도 시간적으로 효과적인 작업이 필요한 응용 분야에 적용하기 어렵다. 본 논문에서는 사람의 손가락 동작을 기반으로 드론의 정밀하면서도 간편한 제어가 가능한 모션 인식 체계를 설계한다. 손가락 모션 인식 기반의 드론제어는 드론 축구 등과 같이 신속성과 정밀성이 필요한 분야의 서비스로 확장될 수 있다.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.549-556
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• 2001

The general problems of recovering 3D for 2D imagery require the depth information for each picture element form focus. The manual creation of those 3D models is consuming time and cost expensive. The goal in this paper is to simplify the depth estimation algorithm that extracts the depth information of every region from monocular image sequence with camera translation to implement 3D video in realtime. The paper is based on the property that the motion of every point within image which taken from camera translation depends on the depth information. Full-search motion estimation based on block matching algorithm is exploited at first step and ten, motion vectors are compensated for the effect by camera rotation and zooming. We have introduced the algorithm that estimates motion of object by analysis of monocular motion picture and also calculates the averages of frame depth and relative depth of region to the average depth. Simulation results show that the depth of region belongs to a near object or a distant object is in accord with relative depth that human visual system recognizes.

• Annual Conference of KIPS
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• 2015.10a
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• pp.1563-1566
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• 2015

보안과 관련된 기술이 발전하여 대규모의 장소에 적합한 보안시스템들이 많이 개발되었다. 특히 CCTV를 이용한 감시카메라의 형태도 다양화되었다. 스마트폰의 어플리케이션이나 웹을 통해서 어디서든 감시할 수도 있어, 이를 통해 보안사고 시에 빠른 대처가 가능하다. 하지만 대규모 시스템이 아닌 경우에는 침입자 발견이 늦고, 뒤늦은 대처로 인해 큰 피해가 발생할 수 있다. 라즈베리파이, 실드 보드 등 기타 하드웨어들을 통하여 침입자를 스스로 감지하여 사용자에게 즉시 알림을 전송함으로써 보안사고에 대한 대처를 빠르고 효율적으로 할 수 있는 보안카메라를 구현하였다. 본 보안 시스템은 소리의 방향을 계산하고 정확한 방향으로의 보정을 통하여 최초 침입자를 인식한다. 이후 이미지트래킹을 통하여 침입자를 추적한다. 무선 네트워크를 이용하기 때문에 네트워크가 지원되는 어느 장소에서든지 사용이 가능하다. 대규모 보안시스템을 설치할 여건이 되기 어려운 작은 공장, 상가, 사무실 등에서 보안시스템으로 사용되면 유용할 것이다. 자세한 개발 내용은 본문에 기술한다.

• Journal of IKEEE
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• v.23 no.1
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• pp.44-52
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• 2019

We implement an educational indoor autonomous mobile robot system that integrates LiDAR sensing information with RGB-D camera image information and exploits the integrated information. This system uses the existing sensing method employing a LiDAR with a small number of scan channels to acquire LiDAR sensing information. To remedy the weakness of the existing LiDAR sensing method, we propose the 3D structure recognition technique using depth images from a RGB-D camera and the deep learning based object recognition algorithm and apply the proposed technique to the system.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1129-1135
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• 2013

A new vehicle detection algorithm based on the multiple optimal Adaboost classifiers with optimal feature selection is proposed. It consists of two major modules: 1) Theoretical DDISF(Distance Dependent Image Scaling Factor) based image scaling by site modeling of the installed cameras. and 2) optimal features selection by Haar-like feature analysis depending on the distance of the vehicles. The experimental results of the proposed algorithm shows improved recognition rate compare to the previous methods for vehicles and non-vehicles. The proposed algorithm shows about 96.43% detection rate and about 3.77% false alarm rate. These are 3.69% and 1.28% improvement compared to the standard Adaboost algorithmt.