• Journal of KIISE:Software and Applications
• /
• v.33 no.12
• /
• pp.1022-1030
• /
• 2006

The location awareness is an important part of many ubiquitous computing systems, but a perfect location system does not exist yet in spite of many researches. Among various location tracking systems, we choose the RFID system due to its wide applications. However, the sensed RSSI signal is too sensitive to the direction of a RFID reader antenna, the orientation of a RFID tag, the human interference, and the propagation media situation. So, the existing location tracking method in spite of using the particle filter is not working well. To overcome this shortcoming, we suggest a robust location tracking method with a double layered structure, where the first layer coarsely estimates a tag's location in the block level using a regression technique or the SVM classifier and the second layer precisely computes the tag's location, velocity and direction using the particle filter technique. Its layered structure improves the location tracking performance by restricting the moving degree of hidden variables. Many extensive experiments show that the proposed location tracking method is so precise and robust to be a good choice for implementing the location estimation of a person or an object in the ubiquitous computing. We also validate the usefulness of the proposed location tracking method by implementing it for a real-time people monitoring system in a noisy and complicate workplace.

• Journal of Internet Computing and Services
• /
• v.8 no.2
• /
• pp.15-22
• /
• 2007

As we detect defects and eliminate them in early stages, we can make better quality software. For doing this task, we need to use a defect tracking system which con effectively track and manage defects that give severe effects on software quality. Those existing defect tracking systems have some weaknesses as we apply them to organizations that use CMMI for process improvements. Major problems of those systems are that they require the organizations to collect many types of defect data at a time without providing the proper explanation and even without the support of defect management process. The organizations at CMMI maturity level 2 and 3 have problems for analyzing those defects because there is no specific process area at CMMI maturity level 2 and 3 which directly handles defect managing activites. This paper resolves those problems by developing a defect tracking system which offers methods of managing defects. And the system provides guidelines of which defects should be gathered for each CMMI mathurity levels. The system also has functions to generate various status and statistic information on defects, and to assign defect data to the person in charge so that he or she track the defect to the closure

• Journal of Broadcast Engineering
• /
• v.23 no.2
• /
• pp.274-282
• /
• 2018

Most occupancy sensors installed in buildings, households and so forth are pyroelectric infra-red (PIR) sensors. One of disadvantages is that PIR sensor can not detect the stationary person due to its functionality of detecting the variation of thermal temperature. In order to overcome this problem, the utilization of camera vision sensors has gained interests, where object tracking is used for detecting the stationary persons. However, the object tracking has an inherent problem such as tracking drift. Therefore, the recognition of humans in static trackers is an important task. In this paper, we propose a CNN-based human recognition to determine whether a static tracker contains humans. Experimental results validated that human and non-humans are classified with accuracy of about 88% and that the proposed method can be incorporated into practical vision occupancy sensors.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.8 no.2
• /
• pp.477-483
• /
• 2004

This paper describes preliminary results we have obtained in developing a computer vision system based on active IR illumination for real time gaze tracking for interactive graphic display. Unlike most of the existing gaze tracking techniques, which often require assuming a static head to work well and require a cumbersome calibration process for each person our gaze tracker can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using the Generalized Regression Neural Networks(GRNN). With GRNN, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Futhermore, the mapping function can generalize to other individuals not used in the training. The effectiveness of our gaze tracker is demonstrated by preliminary experiments that involve gaze-contingent interactive graphic display.

• Asia Marketing Journal
• /
• v.17 no.4
• /
• pp.21-42
• /
• 2016

Purpose: Social influence has a decisive role in shaping a person's cognition and behavior. Chinese face consciousness, including moral component, is an important part of Chinese traditional culture, which influences people to implement moral behavior. With both eye-tracking technology and traditional questionnaire, this research aims to explore people's moral psychology and the psychological processing mechanisms of Chinese face consciousness, as well as the impact of Chinese face consciousness on the preference for the ecological product. Method and Data: 75 college and MBA students' eye movement data were collected when they read different kinds of moral materials, as well as data from the subsequent questionnaires. To test the hypothesis, ANOVA analysis and Heat Map analysis were performed. Besides, the PROCESS of bootstrap was used to test mediation effect. Findings: The results reveal that: 1. Compared to the moral-situation reading, when subjects read immoral situations, they need more processing time due to the moral dissonance and cognitive load. 2. Compared to the control condition, when threatened moral self is primed, subjects prefer to choose ecological product. 3. Protective face orientation is the mediator between threatened moral self and preference to ecological product. Key Contributions: First, this study broadens the use of eye-tracking technology in marketing and demonstrates a better understanding of the relationship between morality and consumer behavior in a more scientific way. Second, this study not only distinguishes the meanings between "protective face orientation" and "acquisitive face orientation", but also innovatively validates that when moral self is threatened, consumers tend to choose ecological product as moral compensation in order to protect their face. It can shed light on the promotion of ecological product in practical applications.

• The KIPS Transactions:PartB
• /
• v.16B no.5
• /
• pp.385-394
• /
• 2009

Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.3
• /
• pp.1-5
• /
• 2021

Sleep apnea refers to a condition in which a person does not breathe during sleep, and is a dangerous symptom that blocks oxygen supply in the body, causing various complications, and the elderly and infants can die if severe. In this paper, we present an algorithm that classifies sleep breathing by analyzing the intensity of breathing with images alone in preparation for the risk of sleep apnea. Only the chest of the person being measured is set to the Region of Interest (ROI) to determine the breathing strength by the differential image within the corresponding ROI area. The adult was selected as the target of the measurement and the breathing strength was measured accurately, and the difference in breathing intensity was also distinguished using depth information. Two videos of sleeping babies also show that even microscopic breathing motions smaller than adults can be detected, which is also expected to help prevent infant death syndrome (SIDS).

• Journal of Digital Convergence
• /
• v.11 no.2
• /
• pp.243-248
• /
• 2013

This paper proposes an effective method using Adaptive Boosting to track a person's head in complex background. By only one way to feature extraction methods are not sufficient for modeling a person's head. Therefore, the method proposed in this paper, several feature extraction methods for the accuracy of the detection head running at the same time. Feature Extraction for the imaging of the head was extracted using sub-region and Haar wavelet transform. Sub-region represents the local characteristics of the head, Haar wavelet transform can indicate the frequency characteristics of face. Therefore, if we use them to extract the features of face, effective modeling is possible. In the proposed method to track down the man's head from the input video in real time, we ues the results after learning Harr-wavelet characteristics of the three types using AdaBoosting algorithm. Originally the AdaBoosting algorithm, there is a very long learning time, if learning data was changes, and then it is need to be performed learning again. In order to overcome this shortcoming, in this research propose efficient method using cascade AdaBoosting. This method reduces the learning time for the imaging of the head, and can respond effectively to changes in the learning data. The proposed method generated classifier with excellent performance using less learning time and learning data. In addition, this method accurately detect and track head of person from a variety of head data in real-time video images.

• Journal of the Ergonomics Society of Korea
• /
• v.29 no.4
• /
• pp.655-659
• /
• 2010

The objective of this study is to quantitatively incorporate user observation into usability evaluation of mobile interfaces using monitoring techniques in first- and third-person points of view. In this study, an experiment was conducted to monitor and record users' behavior using Ergoneers Dikablis, a gaze tracking device. The experiment was done with 2 mobile phones each with a button keypad interface and a touchscreen interface for comparative analysis. The subjects included 20 people who have similar experiences and proficiency in using mobile devices. Data from video recordings were coded with Noldus Observer XT to find usage patterns and to gather quantitative data for analysis in terms of effectiveness, efficiency and satisfaction. Results showed that the button keypad interface was generally better than the touchcreen interface. The movements of the fingers and gaze were much simpler when performing given tasks on the button keypad interface. While previous studies have mostly evaluated usability with performance measures by only looking at task results, this study can be expected to contribute by suggesting a method in which the behavioral patterns of interaction is evaluated.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.9C
• /
• pp.756-760
• /
• 2010

This paper is proposed to an novel facial behavior recognition system for driver's fatigue detection. Facial behavior is shown in various facial feature such as head expression, head pose, gaze, wrinkles. But it is very difficult to clearly discriminate a certain behavior by the obtained facial feature. Because, the behavior of a person is complicated and the face representing behavior is vague in providing enough information. The proposed system for facial behavior recognition first performs detection facial feature such as eye tracking, facial feature tracking, furrow detection, head orientation estimation, head motion detection and indicates the obtained feature by AU of FACS. On the basis of the obtained AU, it infers probability each state occur through Bayesian network.