• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.247-253
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• 2019

In this paper, we present a new method for real-time tracking of human walking around a laser sensor system. The method converts range data with $r-{\theta}$ coordinates to a 2D image with x-y coordinates. Then human tracking is performed using human's features, i.e. appearances of human walking pattern, and the input range data. The laser sensor based human tracking method has the advantage of simplicity over conventional methods which extract human face in the vision data. In our method, the problem of estimating 2D positions and orientations of two walking human's ankle level is formulated based on a moving trajectory algorithm. In addition, the proposed tracking system employs a HMM to robustly track human in case of occlusions. Experimental results using a real system demonstrate usefulness of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2579-2585
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• 2010

This paper proposes a dynamic human pose tracking method using motion-based search strategy from an image sequence obtained from a monocular camera. The proposed method compares the image features between 3D human model projections and real input images. The method repeats the process until predefined criteria and then estimates 3D human pose that generates the best match. When searching for the best matching configuration with respect to the input image, the search region is determined from the estimated 2D image motion and then search is performed randomly for the body configuration conducted within that search region. As the 2D image motion is highly constrained, this significantly reduces the dimensionality of the feasible space. This strategy have two advantages: the motion estimation leads to an efficient allocation of the search space, and the pose estimation method is adaptive to various kinds of motion.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.423-430
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• 2013

In typical face recognition systems, the frontal view of face is preferred to reduce the complexity of the recognition. Thus individuals may be required to stare into the camera, or the camera should be located so that the frontal images are acquired easily. However these constraints severely restrict the adoption of face recognition to wide applications. To alleviate this problem, in this paper, we address the problem of tracking and recognizing faces in video captured with no environmental control. The face tracker extracts a sequence of the angle/size normalized face images using IVT (Incremental Visual Tracking) algorithm that is known to be robust to changes in appearance. Since no constraints have been imposed between the face direction and the video camera, there will be various poses in face images. Thus the pose is identified using a PCA (Principal Component Analysis)-based pose classifier, and only the pose-matched face images are used to identify person against the pre-built face DB with 5-poses. For face recognition, PCA, (2D)PCA, and $(2D)^2PCA$ algorithms have been tested to compute the recognition rate and the execution time.

• 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 Internet Computing and Services
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• v.9 no.3
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• pp.91-101
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• 2008

This paper proposes component-based density propagation for tracking a component-based human body model that comprises components and their flexible links. We divide a human body into six body parts as components - head, body, left arm, right arm, left foot, and right foot - that are most necessary in tracking its movement. Instead of tracking a whole body's silhouette, using component-based density propagation, the proposed method individually tracks each component of various parts of human body through a human body model connecting the components. The proposed human body tracking system has been applied to track movements usee for young children's movement education: balancing, hopping, jumping, walking, turning, bending, and stretching. This proposed system demonstrated the validity and effectiveness of movement tracking by independently detecting each component in the human body model and by acquiring an average 97% of high tracking rate.

• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.65-74
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• 2006

Human tracking is a requirement for the advanced human-computer interface (HCI), This paper proposes a method which uses a component-based human model, detects body parts, estimates human postures, and animates an avatar, Each body part consists of color, connection, and location information and it matches to a corresponding component of the human model. For human tracking, the 2D information of human posture is used for body tracking by computing similarities between frames, The depth information is decided by a relative location between components and is transferred to a moving direction to build a 2-1/2D human model. While each body part is modelled by posture and directions, the corresponding component of a 3D avatar is rotated in 3D using the information transferred from the human model. We achieved 90% tracking rate of a test video containing a variety of postures and the rate increased as the proposed system processed more frames.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.600-607
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• 2008

This paper proposes a robust human detection method irrespective of their pose variation using the multiple oriented 2D elliptical filters (MO2DEFs). The MO2DEFs can detect the humans regardless of their poses unlike existing object oriented scale adaptive filter (OOSAF). To overcome OOSAF's limitation, we introduce the MO2DEFs whose shapes look like the oriented ellipses. We perform human detection by applying four different 2D elliptical filters with specific orientations to the 2D spatial-depth histogram and then by taking the thresholds over the filtered histograms. In addition, we determine the human pose by using convolution results which are computed by using the MO2DEFs. We verify the human candidates by either detecting the face or matching head-shoulder shapes over the estimated rotation. The experimental results showed that the accuracy of pose angle estimation was about 88%, the human detection using the MO2DEFs outperformed that of using the OOSAF by $15{\sim}20%$ especially in case of the posed human.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.725-730
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• 2007

본 논문에서는 인체 추적에 필요한 인체의 각 부위들을 구성요소로 각각 검출하여 연결하는 인체 모델을 통해 각 구성요소를 개별적으로 추정하게 된다. 여기서 인체의 구성요소 중 동작 추적에 가장 필요한 6개 부위로 구성된 구성요소인 머리, 몸통, 왼팔, 오른팔, 왼발, 오른발 등을 검출하여 추적한 후, 각 구성요소의 중심값과 색상정보를 이용하여 이전 프레임과 현재 프레임 간에 연결성을 두여 각 구성요소를 개별적으로 확률 전파를 통해 추적되어지고, 각 구성요소의 추적 결과는 구성요소들의 추정 결과를 구성요소 기반 확률 전파를 이용하여 인체의 동작을 추정하는 방법을 제안한다. 입력 영상에서 피부색 등의 색상 정보를 이용하여 인체 부위 또는 인체 모델의 구성 요소들 각각의 중심값과 색상정보를 가지고 확률전파를 통해 이것이 어떤 동작인지 동작 추정이 가능하다. 본 논문에서 제안하는 인체 동작 추적 시스템은 유아의 동작교육에 이용되는 7가지 동작인 걷기, 뛰기, 앙감질, 구부리기, 뻗기, 균형 잡기, 회전하기 등에 적용하였다. 본 논문에서 제안한 인체 모델의 각 구성요소 부위들을 독립적으로 검출하여 평균 96%의 높은 인식률을 나타냈고, 앞서 적용한 7가지 동작에 대해서 실험한 결과 평균 88.5% 성공률을 획득함으로써 본 논문에서 제안한 방법의 타당성을 보였다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.487-491
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• 2008

카메라로부터 얻어지는 화상정보를 처리하여 사람을 검지하는 기술은 많은 분야에 적용될 수 있다. 실제로 많은 어플리케이션에 적용되고 있다. 현재 Tracking 기술에 관련한 다양한 논문과 방법들이 존재한다. 본 논문에서는 스테레오비전이 아닌 2-D조건에서 움직이는 물체와 움직이지 않는 물체를 구분하여, 구분된 영역에서 탬플릿 매칭을 통하여 사람 검지여부를 결정하는 알고리즘을 제안한다.