• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.660-661
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• 2015

In this paper, we describe the output of specific situation through the recognition of several situation in the school zone based school. In the school zone, there needs the method of situation recognition to react to the diverse event or accident, and the situation output should be requested.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.51-55
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• 2015

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing dangerous situation. The proposed method firstly extracts the facial region using Harr-like technique from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, detects facial expression, and recognizes dangerous situation. The proposed method is evaluated for MUCT database image and web cam input. The proposed method produces good results of facial expression and discriminates dangerous situation well and the average recognition rate is 91.05%.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.556-565
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• 2014

This paper proposes a method to track object of real-time video transferred through single web-camera and to recognize risk-situation and human actions. The proposed method recognizes human basic actions that human can do in daily life and finds risk-situation such as faint and falling down to classify usual action and risk-situation. The proposed method models the background, obtains the difference image between input image and the modeled background image, extracts human object from input image, tracts object's motion and recognizes human actions. Tracking object uses the moment information of extracting object and the characteristic of object's recognition is moment's change and ratio of object's size between frames. Actions classified are four actions of walking, waling diagonally, sitting down, standing up among the most actions human do in daily life and suddenly falling down is classified into risk-situation. To test the proposed method, we applied it for eight participants from a video of a web-cam, classify human action and recognize risk-situation. The test result showed more than 97 percent recognition rate for each action and 100 percent recognition rate for risk-situation by the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.197-200
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• 2014

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing risk situation. The proposed method firstly extracts the facial region from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, discriminates facial expression, and recognizes risk situation. The proposed method is evaluated for Cohn-Kanade database image. The proposed method produces good results of facial expression and discriminates risk situation well.

• Speech Sciences
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• v.6
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• pp.219-233
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• 1999

This paper presents a real-time implementation of a speaker independent speech recognition system based on a discrete hidden markov model(DHMM). This system is developed for a car navigation system to design on-chip VLSI system of speech recognition which is used by fixed point Oak DSP core of DSP GROUP LTD. We analyze recognition procedure with C language to implement fixed point real-time algorithms. Based on the analyses, we improve the algorithms which are possible to operate in real-time, and can verify the recognition result at the same time as speech ends, by processing all recognition routines within a frame. A car noise is the colored noise concentrated heavily on the low frequency segment under 400 Hz. For the noise robust processing, the high pass filtering and the liftering on the distance measure of feature vectors are applied to the recognition system. Recognition experiments on the twelve isolated command words were performed. The recognition rates of the baseline recognizer were 98.68% in a stopping situation and 80.7% in a running situation. Using the noise processing methods, the recognition rates were enhanced to 89.04% in a running situation.

• International Journal of Contents
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• v.16 no.3
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• pp.1-17
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• 2020

Human motion recognition is essential for user-centric services such as surveillance-based security, elderly condition monitoring, exercise tracking, daily calories expend analysis, etc. It is typically based on the movement data analysis such as the acceleration and angular velocity of a target user. The existing motion recognition studies are only intended to measure the basic information (e.g., user's stride, number of steps, speed) or to recognize single motion (e.g., sitting, running, walking). Thus, a new mechanism is required to identify the transition of single motions for assessing a user's consecutive motion more accurately as well as recognizing the user's body and surrounding situations arising from the motion. Thus, in this paper, we collect the human movement data through Android smartphones in real time for five targeting single motions and propose a mechanism to recognize a consecutive motion including transitions among various motions and an occurred situation, with the state transition model to check if a vulnerable (life-threatening) condition, especially for the elderly, has occurred or not. Through implementation and experiments, we demonstrate that the proposed mechanism recognizes a consecutive motion and a user's situation accurately and quickly. As a result of the recognition experiment about mix sequence likened to daily motion, the proposed adoptive weighting method showed 4% (Holding time=15 sec), 88% (30 sec), 6.5% (60 sec) improvements compared to static method.

• Journal of the Korean Home Economics Association
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• v.35 no.1
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• pp.155-168
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• 1997

This study explored the effect of fathers' childrearing participation and their recognition about child care situation on fathers' satisfaction with nonparental child care. The results of this study are as follows: First fathers who had finished college education whose family had more family income and whose wives had jobs were more participated in childrearing practice than fathers who had finished high school whose family had less family income and whose wives had no job, Second when childre's age were over 5 years fathers tended to recognize about child care situation. That is fathers whose children were over 5 years old more recognized about how caregivers took care for children's congnitive development and were more satisfied with educational materials of child care centers than fathers whose children were less 5 years old. Third fathers' recognition about child care situation were found to mediate the relationship between fathers' childrearing participation and fathers' satisfaction with nonparental child care.

• School Mathematics
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• v.2 no.2
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• pp.509-543
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• 2000

The purpose of this paper is to study the present situation of the performance assessment having been introduced for recent years as a new system to measure students' mathematical power as well as their achievement in mathematics by analyzing math teachers' recognition about the assessment. The specific research questions are as follows: (1) what is the difficulty in the administrative process of the assessment\ulcorner (2) what is mathematics teachers' recognition about the assessment with respect to its nature and purpose\ulcorner (3) what is the situation of the in-service program for the newly introduced assessment\ulcorner (4) what is the administrative procedure of the assessement\ulcorner (5) what is mathematics teachers' recognition about the effectiveness of the assessemtn\ulcorner (6) What support is requried to improve the assessemt?

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.345-354
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• 2021

Recently, deep learning-based image recognition systems have been adopted to various surveillance environments, but most of them are still picture-type object recognition methods, which are insufficient for the long term temporal analysis and high-dimensional situation management. Therefore, we propose a method recognizing the specific dangerous situation generated by human in real-time, and utilizing deep learning-based object analysis techniques. The proposed method uses deep learning-based object detection and tracking algorithms in order to recognize the situations such as 'trespassing', 'loitering', and so on. In addition, human's joint pose data are extracted and analyzed for the emergent awareness function such as 'falling down' to notify not only in the security but also in the emergency environmental utilizations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.666-667
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• 2015

The situation recognition can be implemented by configuring several situation in the school zone system. Such the recognized situations can use the result informing to the event through appropriate output format. In this paper, we propose the design scheme of situation output in the school zone system.