• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.129-135
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• 2022

Real human digital models appear more and more frequently in VR/AR application scenarios, in which real-time markerless face capture animation of personalized virtual human faces is an important research topic. The traditional way to achieve personalized real human facial animation requires multiple mature animation staff, and in practice, the complex process and difficult technology may bring obstacles to inexperienced users. This paper proposes a new process to solve this kind of work, which has the advantages of low cost and less time than the traditional production method. For the personalized real human face model obtained by 3D reconstruction technology, first, use R3ds Wrap to topology the model, then use Avatary to make 52 Blend-Shape model files suitable for AR-Kit, and finally realize real-time markerless face capture 3D real human on the UE4 platform facial motion capture, this study makes rational use of the advantages of software and proposes a more efficient workflow for real-time markerless facial motion capture of personalized 3D real human models, The process ideas proposed in this paper can be helpful for other scholars who study this kind of work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.84-95
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• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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• pp.1-9
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• 2000

This paper deals with the difference between human sensibility collected from real product and that through graphic image(photographs and graphic images on CRT monitor) on the same automotive interior. The objective of this study is to verify the possibility that, If there are some restrictions in collecting human sensibility through real product directly, they can be overcome by using graphic image instead, making it easy to collect and analyze human sensibility so as to reflect consumers sensibility in the design of automotive interior, and also comparing the result between real product and graphic image on CRT monitor in order to confirm the potentiality of developing a remote human sensibility survey system through Internet. Therefore two experiments were conducted and the object for experiments was limited to automotive interior. The analysis results showed that there were significant differences between graphic image and real product in case of total interior and IPC(Instrument Panel Center) and no significant difference in case of display panel. Also, there were no significant difference when the subject group was female(housewife). To conclude, we can infer, in case of display panel, that it is possible to replace real product with graphic image to extract similar results on human sensibility and to collect human sensibility through Internet.

• Journal of the Korean Society of Safety
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• v.22 no.3 s.81
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• pp.81-87
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• 2007

In this study digital human models of ship construction tasks using modeling & simulation were constructed and human models' activities through human activity analysis were evaluated. Human Factors experts analyzed the actual workers' tasks using the same technique used in human activity analysis at the same time. The main objective of this study is to check a possibility of applying digital human modeling technique to ship construction tasks that are mostly non-standardized(not uniformed) whereas most applications of digital human modeling technique have been applied to standardized tasks. We evaluated postures of both real workers and digital humans by RULA. It turned out that the final scores of RULA evaluation on real workers are the same as the RULA scores for digital humans. However, there were differences of RULA detail scores between real workers and digital humans in the several processes related with the wrist twist and deviations. Those differences are considered to be resulted from the error in the on-site measuring worker's body dimension which could be reduced by accurate tools to correct data for body dimension and digital real drawings for facilities. The results showed possibility of application of digital human modeling and ergonomic analysis on informal work operations as well as formal operations in the shipbuilding industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.209-216
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• 2009

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• ETRI Journal
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• v.35 no.6
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• pp.949-959
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• 2013

Since the recent launch of Microsoft Xbox Kinect, research on 3D human pose estimation has attracted a lot of attention in the computer vision community. Kinect shows impressive estimation accuracy and real-time performance on massive graphics processing unit hardware. In this paper, we focus on further reducing the computation complexity of the existing state-of-the-art method to make the real-time 3D human pose estimation functionality applicable to devices with lower computing power. As a result, we propose two simple approaches to speed up the random-forest-based human pose estimation method. In the original algorithm, the random forest classifier is applied to all pixels of the segmented human depth image. We first use a multi-scale approach to reduce the number of such calculations. Second, the complexity of the random forest classification itself is decreased by the proposed cascade approach. Experiment results for real data show that our method is effective and works in real time (30 fps) without any parallelization efforts.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.444-448
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• 2008

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2:image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.977-983
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• 2005

In this paper, novel ubiquitous concept of real reality robot game controlled by a mobile server robot is proposed. Real reality robot game means that two real robots controlled by humans/computers through the internet are playing a boxing game. The mobile server robot captures playing images of the boxing game and sends them to GUI on the screen of human operators' PC. The human operator can login to the boxing game from any computer in any place if he/she is permitted. Remote control of a boxing robot by a motion capture system through network is implemented. Successful motion control of a boxing robot remotely controlled by a motion capture system through network can be achieved. In addition, real boxing games between a human and a computer are demonstrated.

• Journal of the Ergonomics Society of Korea
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• v.20 no.1
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• pp.1-13
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• 2001

Conventionally the subjective assessment for measuring the human sensibility is always performed after an experiment or a stimulus is terminated. This aftermath method can not. exactly, reflect the human sensibility which is produced at the moment of the presentation of the stimulus. In the present study, a new real-time subjective assessment system is developed. The system is composed of two parts: the sensibility input part and the sensibility evaluation part. The sensibility input part gets the values, which are recorded on the input board using pen-mouse, from the evaluation of each subject on his/her own subjective sensibility for the stimulus. The sensibility evaluation part displays the level of the pleasantness and arousal on one and two dimension in real time. An experiment on the design of the input board that any subject can easily and exactly evaluates one's own sensibility is also included in this study. The system can be used for evaluating the human subjective sensibility in real time, and, also, can be applied to other subjective assessment tests that require real time evaluation.