• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.100.3-100
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• 2002

This paper presents the method for detection and tracking of multiple humans robustly in mobile platform. The perception of human is performed in real time through the processing of images acquired from a moving stereo vision system. We performed multi-cue integration such as human shape, skin color and depth information to detect and track each human in moving background scene. Human shape is measured by edge-based template matching on distance transformed image. Improving robustness for human detection, we apply the human face skin color in HSV color space. And we could increase the accuracy and the robustness in both detection and tracking by applying random sampling stochastic estimati...

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.243-262
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• 2022

In this study, eye tracking was used for the appearance of the robot during the social robot design study. During the research, each part of the social robot was designated as AOI (Areas of Interests), and the user's attitude was measured through a design evaluation questionnaire to construct a design research model of the social robot. The data used in this study are Fixation, First Visit, Total Viewed, and Revisits as eye tracking indicators, and AOI (Areas of Interests) was designed with the face, eyes, lips, and body of the social robot. And as design evaluation questionnaire questions, consumer beliefs such as Face-highlighted, Human-like, and Expressive of social robots were collected and as a dependent variable was attitude toward robots. Through this, we tried to discover the mechanism that specifically forms the user's attitude toward the robot, and to discover specific insights that can be referenced when designing the robot.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.266-272
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• 2013

Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1119-1128
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• 2013

In this paper, we propose an algorithm to detect and track the human face with a GPU-based high speed. Basically the detection algorithm uses the existing Adaboost algorithm but the search area is dramatically reduced by detecting movement and skin color region. Differently from detection process, tracking algorithm uses only depth information. Basically it uses a template matching method such that it searches a matched block to the template. Also, In order to fast track the face, it was computed in parallel using GPU about the template matching. Experimental results show that the GPU speed when compared with the CPU has been increased to up to 49 times.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.100.1-100
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• 2001

With the prolific growth of cable, satellite digital broadcasting and internet related industry, new digital contents are being demanded. Today, more end-users seek participations in the media through interactivity. Visual tracking technology, based on image processing, is mainly used in fields of human face tracking, security inspection, and traffic monitoring applications. In this research, we describe the interactive modules such as information display, e1-commerce and other services along with on-screen visuals on the streaming media using object visual tracking technology. The ...

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1171-1176
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• 2014

The eye-tracking [ET] is used on the human computer interaction [HCI] analysing the movement status as well as finding the gaze direction of the eye by tracking pupil's movement on a human face. Nowadays, the ET is widely used not only in market analysis by taking advantage of pupil tracking, but also in grasping intention, and there have been lots of researches on the ET. Although the vision based ET is known as convenient in application point of view, however, not robust in changing environment such as illumination, geometrical rotation, occlusion and scale changes. This paper proposes two steps in the ET, at first, face and eye regions are discriminated by Haar classifier on the face, and then the pupils from the discriminated eye regions are tracked by CAMShift as well as Template matching. We proved the usefulness of the proposed algorithm by lots of real experiments in changing environment such as illumination as well as rotation and scale changes.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.173-176
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• 2008

Among human body parts, the human face has been studied the most actively for the interlace between humans and computers because face has statistic consistency in color, shape and texture. Those characteristics make computers detect and track human faces in images robustly and accurately. The human face consists of eyes, nose, mouth, eyebrows and other features, Detecting and tracking each feature have been researched. The open mouth is the largest in size and the easiest to detect among them, In this study, we present a system which can move mouse pointer using the position and state of the mouth.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.449-471
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• 2012

In intelligent surveillance systems, it is required to robustly track multiple people. Most of the previous studies adopted a Gaussian mixture model (GMM) for discriminating the object from the background. However, it has a weakness that its performance is affected by illumination variations and shadow regions can be merged with the object. And when two foreground objects overlap, the GMM method cannot correctly discriminate the occluded regions. To overcome these problems, we propose a new method of tracking and identifying multiple people. The proposed research is novel in the following three ways compared to previous research: First, the illuminative variations and shadow regions are reduced by an illumination normalization based on the median and inverse filtering of the L*a*b* image. Second, the multiple occluded and overlapped people are tracked by combining the GMM in the still image and the Lucas-Kanade-Tomasi (LKT) method in successive images. Third, with the proposed human tracking and the existing face detection & recognition methods, the tracked multiple people are successfully identified. The experimental results show that the proposed method could track and recognize multiple people with accuracy.

• Journal of information and communication convergence engineering
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• v.3 no.1
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• pp.56-61
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• 2005

In this paper, we present a video rate control scheme for storage and delivery in which the time-varying viewing interests are controlled by human gaze. To track the gaze, the pupil's movement is detected using the three-step process : detecting face region, eye region, and pupil point. To control bit rates, the quantization parameter (QP) is changed by considering the static parameters, the video object priority derived from the pupil tracking, the target PSNR, and the weighted distortion value of the coder. As results, we achieved human interfaced visual model and corresponding region-of-interest rate control system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.887-898
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• 2005

In this paper, we introduce a probabilistic model based on Bayesian networks BNs) for recognizing human fatigue. First of all, we measured face feature information such as eyelid movement, gaze, head movement, and facial expression by IR illumination. But, an individual face feature information does not provide enough information to determine human fatigue. Therefore in this paper, a Bayesian network model was constructed to fuse as many as possible fatigue cause parameters and face feature information for probabilistic inferring human fatigue. The MSBNX simulation result ending a 0.95 BN fatigue index threshold. As a result of the experiment, when comparisons are inferred BN fatigue index and the TOVA response time, there is a mutual correlation and from this information we can conclude that this method is very effective at recognizing a human fatigue.