• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.598-605
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• 2018

Behavior awareness is a technology that recognizes human behavior through data and can be used in applications such as risk behavior through video surveillance systems. Conventional behavior recognition algorithms have been performed using the 2D camera image device or multi-mode sensor or multi-view or 3D equipment. When two-dimensional data was used, the recognition rate was low in the behavior recognition of the three-dimensional space, and other methods were difficult due to the complicated equipment configuration and the expensive additional equipment. In this paper, we propose a method of recognizing human behavior using only CCTV images without additional equipment using only RGB and depth information. First, the skeleton extraction algorithm is applied to extract points of joints and body parts. We apply the equations to transform the vector including the displacement vector and the relational vector, and study the continuous vector data through the RNN model. As a result of applying the learned model to various data sets and confirming the accuracy of the behavior recognition, the performance similar to that of the existing algorithm using the 3D information can be verified only by the 2D information.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.738-744
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• 2013

According to the cognitive science research, the interaction intent of humans can be estimated through an analysis of the representing behaviors. This paper proposes a novel methodology for reliable intention analysis of humans by applying this approach. To identify the intention, 8 behavioral features are extracted from the 4 characteristics in human-human interaction and we outline a set of core components for nonverbal behavior of humans. These nonverbal behaviors are associated with various recognition modules including multimodal sensors which have each modality with localizing sound source of the speaker in the audition part, recognizing frontal face and facial expression in the vision part, and estimating human trajectories, body pose and leaning, and hand gesture in the spatial part. As a post-processing step, temporal confidential reasoning is utilized to improve the recognition performance and integrated human model is utilized to quantitatively classify the intention from multi-dimensional cues by applying the weight factor. Thus, interactive robots can make informed engagement decision to effectively interact with multiple persons. Experimental results show that the proposed scheme works successfully between human users and a robot in human-robot interaction.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.463-471
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• 2023

This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.

• Korean Journal of Human Ecology
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• v.15 no.6
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• pp.919-935
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• 2006

The purpose of this study was to analyze the relationship between outdoor play environments and child's play behaviors. The data were collected by field measurement survey, and non-participatory observation from 9 child care centers in Daejeon. The field measurement survey were conducted from June 20 to July 20, 2004, whereas non-participatory behavior observation were conducted from September 2 to October 10, 2004. The major results showed the following. 1) The characteristics of outdoor play environment in child care centers showed that most of them were too small and did not use the adequate surfacing to play various activities. In many cases, outdoor play settings was made of play equipment setting, play props and manipulative settings, and tree/vegetation. 2) Child's play behavior has been focused on functional play activity and construction play activity. 3) The relationship between outdoor play environments and child's play behaviors showed that small outside play environment with monotonous construction and multi-complex play equipments produced functional play behaviors on children. On the other hand, where various play areas were put together, we could observe relatively diverse play behaviors. However, in some cases, despite the small and monotonous play area, diverse play behaviors were observed. These playgrounds at least differed from others in that they provided the place where multi purpose play was possible. This finding shows that multi purpose play areas can be an alternative in small playground environment.

• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.5
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• pp.743-754
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• 2014

This study identified the internal motives for the purchase behavior of Korean male consumers in multi-brand fashion stores by conducting in-depth interviews with 8 men in their 20s and 30s. All respondents had significant experience with this type of store. Data were analyzed in an inductive way and compared with Bohemianism to interpret and described the results as a recent phenomenon of men's fashion. The five internal motives were extracted from analysis: the pursuit of freedom of expression, the counter-cultural resistance to department stores and domestic fashion brands, which tend to copy designs from international brands, the pursuit of mobility and adventure for trying to search and wear a new fashion style, the pursuit of pleasure through store experience, and the pursuit of artistic value by considering goods purchased in multi-brand fashion stores as artistic and cultural goods.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.23-29
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• 2022

Virtual humans are implemented with dedicated modeling tools like Unity 3D Engine in virtual space (virtual reality, mixed reality, metaverse, etc.). Various human modeling tools have been introduced to implement virtual human-like appearance, voice, expression, and behavior similar to real people, and virtual humans implemented via these tools can communicate with users to some extent. However, most of the virtual humans so far have stayed unimodal using only text or speech. As AI technologies advance, the outdated machine-centered dialogue system is now changing to a human-centered, natural multi-modal system. By using several pre-trained networks, we implemented an emotion-based multi-modal dialogue system, which generates human-like utterances and displays appropriate facial expressions in real-time.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of Mechanical Science and Technology
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• v.19 no.9
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• pp.1742-1752
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• 2005

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.328-335
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• 2022

With the advance of robot capabilities and functionalities, construction robots assisting construction workers have been increasingly deployed on construction sites to improve safety, efficiency and productivity. For close-proximity human-robot collaboration in construction sites, robots need to be aware of the context, especially construction worker's behavior, in real-time to avoid collision with workers. To recognize human behavior, most previous studies obtained 3D human poses using a single camera or an RGB-depth (RGB-D) camera. However, single-camera detection has limitations such as occlusions, detection failure, and sensor malfunction, and an RGB-D camera may suffer from interference from lighting conditions and surface material. To address these issues, this study proposes a novel method of 3D human pose estimation by extracting 2D location of each joint from multiple images captured at the same time from different viewpoints, fusing each joint's 2D locations, and estimating the 3D joint location. For higher accuracy, the probabilistic representation is used to extract the 2D location of the joints, considering each joint location extracted from images as a noisy partial observation. Then, this study estimates the 3D human pose by fusing the probabilistic 2D joint locations to maximize the likelihood. The proposed method was evaluated in both simulation and laboratory settings, and the results demonstrated the accuracy of estimation and the feasibility in practice. This study contributes to ensuring human safety in close-proximity human-robot collaboration by providing a novel method of 3D human pose estimation.

• Journal of KIBIM
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• v.6 no.3
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• pp.34-41
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• 2016

The previous studies argue that using the semantic properties of BIM objects is efficient for simulating the behaviors of autonomous, computer agents, called virtual-users, but such assumption is not proven via evidence-based research approaches. Hence, this present study aims to investigate the empirical effects of a human behavior simulation model equipped the semantics of spaces on the architecture-major students' operation and understanding of the simulation system, compared to a typical path-finding model. To achieve the aim, this study analyzed the survey and interview data, collected in the authentic design projects. The analysis indicates that (1) using a simulation model equipped the semantics of spaces helps the students' operation of the simulation, and (2) it also aids understanding the relationship between the variables of spaces and virtual-users (${\alpha}=0.74$). In addition, the qualitative data inform that the advantages of the simulation model that computes the semantics of spaces stem in the automatic behavioral changes of massive numbers of virtual-users, and efficient detection and activation on the what-if situations. The analysis also reveals that the simulation model has shortcomings in orchestrating the complex data structure between the semantics properties of spaces and virtual-users under multi-sequential scenarios. The results of this study contribute to develop a future design system combining BIM with human behavior simulation.