• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.111-118
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• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.424-431
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• 2020

This paper focused on the development of sports robot that detects a human player and shots a serve ball automatically. When robot technologies apply to the sports machine, the domain problems occurs such as outdoor environments and playing condition to recognize the visual and the vocal modalities. Gaussian mixture model and Kalman filter are used to detect the player's position in the left, right, and depth direction and to avoid the noises caused by the player's posture variation around the net. The sports robot is designed by the pan-tilt structure to shot a serve ball by pneumatic control under the multi layered software architecture. Finally, the proposed tracking and the machine performance are discussed by experimental results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.61-64
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• 2011

There are many applications that require robust and accurate eye tracking, such as human-computer interface(HCI). In this paper, a novel approach for eye tracking with a principal component analysis on rotated face. In the process of iris detection, intensity information is used. First, for select eye region using principal component analysis. Finally, for eye detection using eye region's intensity. The experimental results show good performance in detecting eye from FERET image include rotate face.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.950-953
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• 2007

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.618-632
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• 2010

This paper presents a novel approach to control the augmented reality (AR) objects robustly in a marker-less AR system by fingertip tracking and hand pattern recognition. It is known that one of the promising ways to develop a marker-less AR system is using human's body such as hand or face for replacing traditional fiducial markers. This paper introduces a real-time method to manipulate the overlaid virtual objects dynamically in a marker-less AR system using both hands with a single camera. The left bare hand is considered as a virtual marker in the marker-less AR system and the right hand is used as a hand mouse. To build the marker-less system, we utilize a skin-color model for hand shape detection and curvature-based fingertip detection from an input video image. Using the detected fingertips the camera pose are estimated to overlay virtual objects on the hand coordinate system. In order to manipulate the virtual objects rendered on the marker-less AR system dynamically, a vision-based hand control interface, which exploits the fingertip tracking for the movement of the objects and pattern matching for the hand command initiation, is developed. From the experiments, we can prove that the proposed and developed system can control the objects dynamically in a convenient fashion.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.320-324
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• 2023

As urbanization accelerates and facilities that use energy increase, human life and property damage due to fire is increasing. Therefore, a fire monitoring system capable of quickly detecting a fire is required to reduce economic loss and human damage caused by a fire. In this study, we aim to develop an improved artificial intelligence model that can increase the accuracy of low fire alarms by mixing DeepSORT, which has strengths in object tracking, with the YOLOv5s model. In order to develop a fire detection model that is faster and more accurate than the existing artificial intelligence model, DeepSORT, a technology that complements and extends SORT as one of the most widely used frameworks for object tracking and YOLOv5s model, was selected and a mixed model was used and compared with the YOLOv5s model. As the final research result of this paper, the accuracy of YOLOv5s model was 96.3% and the number of frames per second was 30, and the YOLOv5s_DeepSORT mixed model was 0.9% higher in accuracy than YOLOv5s with an accuracy of 97.2% and number of frames per second: 30.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.215-224
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• 2011

There have been studying many researches to detect human body and to track one with increasing interest on human and computer interaction. In this paper, we propose the algorithm that automatically extracts joints, linked points of human body, using the ratio of human body under single camera and tracks object. The proposed method gets the difference images of the grayscale images and ones of the hue images between input image and background image. Then the proposed method composes the results, splits background and foreground, and extracts objects. Also we standardize the ratio of human body using face' length and the measurement of human body and automatically extract joints of the object using the ratio and the corner points of the silhouette of object. After then, we tract the joints' movement using block-matching algorithm. The proposed method is applied to test video to be acquired through a camera and the result shows that the proposed method automatically extracts joints and effectively tracks the detected joints.

• Journal of KIISE
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• v.43 no.6
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• pp.645-652
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• 2016

Joint Attention (JA) has been known to play a key role in human social learning. However, relative impact of different interaction types has yet to be rigorously examined because of limitation of existing methodologies to simulate human-to-human interaction. In the present study, we designed a new JA paradigm with emulating human-avatar interaction and virtual reality technologies, and tested the paradigm in two experiments with healthy adults. Our results indicated that initiating JA (IJA) condition was more effective than responding JA (RJA) condition for social learning in both head and hand interactions. Moreover, the hand interaction involved better information processing than the head interaction. The implication of the results, the validity of the new paradigm, and limitations of this study were discussed.

• Journal of the Ergonomics Society of Korea
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• v.17 no.3
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• pp.13-22
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• 1998

Aircraft and the three-dimensional environment in which they operate are not user-friendly for human beings. As a result, maintaining the proficiencies necessary to safely and efficiently fly an airplane are difficult, and costly. The physiological and emotional status of the human element remains crucial in maintaining safe performance by all crew members. In the study of Hagiwara et al.(1993). they called the physiological and emotional status of the human element into the human alertness or physiological activity and stress, fatigue, circadian rhythm, alcohol. smoking, and self-medication are known the major factors that deteriorate the human alertness. Accordingly. this paper deals with the quantitative and objective performance test based on tracking error and reaction time by means of the new computer test program into which the perception-motion system of human beings is applied. Throughout this experiment using performance test, the results suggest that performance capability in state of sleep deprivation 2 hours and alcoholic 0.05~0.06% in blood were more impaired than one in a normal state, and they further showed statistically significant differences between them, which were influenced by impairment factors of body regulation and pilot's grade. We also obtained the prediction value and the 95% confidence interval of tracking error and reaction time at the normal state for the purpose of distinguishing performance capability between the normal state and the abnormal state. And it is expected that the evaluation of human alertness using performance test will be applied to the quantitative assessment of an each pilot's realistic consciousness/attention, and will lead a flight commander to the accurate decision of mission approval prior to a flight.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1070-1075
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• 2013

In this research, we describe a security robot system and ongoing research results to control human's wrong direction in order to forbid human to enter security zone. Proposed robot system surveils a security area with equipped laserscanner sensor usually. When it detect walking human who is for the area, robot calculates his velocity vector, plans own path to forestall and interrupts him who want to head restricted area and starts to move along the estimated trajectory. The walking human is assumed to be a point-object and projected onto an scanning plane to form a geometrical constraint equation that provides position data of the human based on the kinematics of the mobile robot. While moving the robot continues these processes for adapting change of situation. After arriving at an opposite position human's walking direction, the robot advises him not to be headed more and change his course. The experimental results of estimating and tracking of the human in the wrong direction with the mobile robot are presented.