• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.39-46
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• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.642-655
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• 2018

Occupancy sensors installed in buildings and households turn off the light if the space is vacant. Currently PIR(pyroelectric infra-red) motion sensors have been utilized. Recently, the researches using camera sensors have been carried out in order to overcome the demerit of PIR that cannot detect stationary people. The detection of moving and stationary people is a main functionality of the occupancy sensors. In this paper, we propose an on-line human occupancy tracking method using convolutional neural network (CNN) and self-organizing map. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. Using videos capurted from an overhead camera, experiments have validated that the proposed method effectively tracks human.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.255-262
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• 2012

Human motion tracking algorithm is receiving attention from many research areas, such as human computer interaction, video conference, surveillance analysis, and game or entertainment applications. Over the last decade, various tracking technologies for each application have been demonstrated and refined among them such of real time computer vision and image processing, advanced man-machine interface, and so on. In this paper, we introduce cost-effective and real-time human motion tracking algorithms based on depth image 3D point matching with a given superellipsoid body representation. The body representative model is made by using parametric volume modeling method based on superellipsoid and consists of 18 articulated joints. For more accurate estimation, we exploit initial inverse kinematic solution with classified body parts' information, and then, the initial pose is modified to more accurate pose by using 3D point matching algorithm.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.137-140
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• 2008

본 논문에서는 응급상황에 직면한 사람의 위치를 빠르고 정확하게 추적 하기 위하여 광역의 위치추적 기술과 새롭게 제안하는 근거리 위치추적 기술인 능동형 위치추적기법이 융합된 신 개념의 긴급 sos 시스템을 제시하였으며 응급한 상황에 직면한 사람이 소지하는 초소형, 저전력 응급신호 발생장치(Beacon)의 동작 특성을 분석하였다. 그리고 Beacon의 동작 특성 분석을 기반으로 통신 칩의 전력 소모를 최소화 하기 위한 기술을 제안 하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.53-56
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• 2006

최근 가정용 로봇 연구가 활발히 진행됨에 따라, 가정과 같은 다이내믹한 환경에서 로봇이 목소리를 포함한 음원에 반응하고 그 위치를 정확히 찾아가는 것이 매우 중요해지고 있다. 인간이 목표물에 도달하기 위해 경로를 선택할 때, 그 목표물이 소리인 경우는 현재 위치에서 음원의 방향을 추적한다. 또한 그 목표물의 위치가 지도로 주워질 경우에는 현재 위치와 목표불의 절대적 방위를 기준으로 추적한다. 본 논문에서는 이처럼 사람이 다른 사람의 목소리를 듣고 반응하거나 어떤 방향으로 가고자할 때 소리가 나는 방향이나 지도를 통해 대략 자신만의 방위를 만드는 것에 착안하여, 지능형 모바일 로봇에 음원추적 장치와 전자나침반을 장착함으로써 음원의 방향 또는 절대 방위를 기준으로 목표물을 찾아가는 알고리즘을 제시하고자 한다.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.937-944
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• 2007

In this paper, we propose multiple human tracking with an moving stereo camera. The tracking process is based on mean shift algorithm which is using color information of the target. Color based tracking approach is invariant to translation and rotation of the target but, it has several problems. Because of mean shift uses color distribution, it is sensitive to color distribution of background and targets. In order to solve this problem, we combine color and depth information of target. Also, we build human body part model to handle occlusions and we have created adaptive box scale. As a result, the proposed method is simple and efficient to track multiple humans in real time.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.101-104
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• 2011

본 논문에서는 레이저 센서를 장착한 자율 이동 로봇을 이용하여 실내에서 장애물들을 검출한다. 데이터에서 나오는 패턴을 인식해 사람과 정적 장애물을 실시간으로 구분한 후 사람의 속도와 로봇의 속도를 각각 비교하여 따로 지정해준 안전거리를 유지하며 주행한다. 예상치 못한 상황이 발생될 것을 대비해 로봇의 전방에 범퍼 센서를 장착하여 안전성을 고려하였다. 로봇의 자기 위치 인식을 위해 StarGazer센서를 이용하였다. 패턴은 레이저 센서 데이터의 거리, 각 값을 이용하여 다리 패턴의 너비를 구하고 너비의 가운데 점을 중심점으로 지정해 추적하며 구동하는 알고리즘을 구현하였다.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2717-2719
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• 2000

사용자의 의도를 파악하는 여러 가지 행동양식 중에서 우리가 관심을 두고 있는 시스템은 사람의 눈 움직임 검출을 이용한 시스템이다. 사람의 눈 움직임에 대한 검출과 추적이 가능하다면 그 적용 분야는 매우 광범위하다. 예를 들면, 일반인들의 컴퓨터 조작을 더 편리하게 할 수도 있고 손을 사용할 수 없는 장애인들의 의사소통이나 정보교환의 한 방법으로 사용될 수 있다. 또한 사람들이 대부분의 정보를 시각적인 면으로 획득한다는 것을 감안할 때 원격 작업의 모니터링과 같은 여러 산업부분이나 군사부분과 같은 분야에 적용될 수 있다. 본 논문에서는 눈의 특징점들을 검출하고 추적하기 위해서 홍채 모델을 설정하고 그 모델이 카메라를 통해 받아들여지는 입력 영상과 일치시키는 과정으로, 카메라의 입력 영상에서 3가지의 기본 영상을 추출하고 모델의 매칭 정도를 판단할 수 있는 매칭 함수를 규정하고 그 함수들을 통하여 홍채 모델을 일치시키는 알고리즘을 제안하고 그 타당성을 보이고자 한다.

• Journal of KIISE:Information Networking
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• v.29 no.1
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• pp.97-107
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• 2002

In general, under interactive broadcasting environment, user finds additional service using top-down menu. However, user can't know that additional service provides information until retrieval has finished and top-down menu requires multi-level retrieval. This paper proposes the new method for additional service providing not using top-down menu but using object selection. For the purpose of this method, the movie of a MPEG should be synchronized with the object information(position, size, shape) and object tracking technique is required. Synchronization technique uses the Directshow provided by the Microsoft. Object tracking techniques use a motion-based tracking and a model-based tracking together. We divide object into two parts. One is face and the other is substance. Face tracking uses model-based tracking and Substance uses motion-based tracking base on the block matching algorithm. To improve precise tracking, motion-based tracking apply the temporal prediction search algorithm and model-based tracking apply the face model which merge ellipse model and color model.