• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.33-40
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• 2023

As South Korea enters the realm of an super-aging society, the demand for elderly welfare services has been steadily rising. However, the current shortage of welfare personnel has emerged as a social issue. To address this challenge, there is active research underway on elderly care robots designed to mitigate the social isolation of the elderly and provide emergency contact capabilities in critical situations. Nonetheless, these functionalities require direct user contact, which represents a limitation of conventional elderly care robots. In this paper, we propose a solution to overcome these challenges by introducing a care robot system capable of interacting with users without the need for direct physical contact. This system leverages commercialized elderly care robots and cameras. We have equipped the care robot with an edge device that incorporates facial expression recognition and action recognition models. The models were trained and validated using public available data. Experimental results demonstrate high accuracy rates, with facial expression recognition achieving 96.5% accuracy and action recognition reaching 90.9%. Furthermore, the inference times for these processes are 50ms and 350ms, respectively. These findings affirm that our proposed system offers efficient and accurate facial and action recognition, enabling seamless interaction even in non-contact situations.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.87-98
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• 2012

Understanding agent's intent is an essential component of the human-computer interaction of ubiquitous computing. Because correct inference of subject's intention in ubiquitous computing system helps particularly to understand situations that involve collaboration among multiple agents or detection of situations that can pose a particular activity. This paper, inspired by people have a mechanism for interpreting one another's actions and for inferring the intentions and goals that underlie action, proposes an approach that allows a computing system to quickly recognize the intent of agents based on experience data acquired through prior capabilities of activities recognition. To proceed intention recognition, proposed method uses formulations of Hidden Markov Models (HMM) to model a system's prior experience and agents' action change, then makes for system infer intents in advance before the agent's actions are finalized while taking the perspective of the agent whose intent should be recognized. Quantitative validation of experimental results, while presenting an accurate rate, an early detection rate and a correct duration rate with detecting the intent of several people performing various activities, shows that proposed research contributes to implement effective intent recognition system.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.21-30
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• 2021

Unlike behaviors that are generally known, information regarding unspecific behaviors is insufficient. For an education or guidance regarding the unspecific behaviors, collection and management of data regarding the unspecific behaviors of special-needs students are needed. In this paper, a consultation management model based on behavior classification of special-needs students using machine learning is proposed. It collects data by photographing the behavior of special students in real time, analyzes the behavior pattern, composes a data set, and trains it in the suggestion system. It is possible to improve the accuracy by comparing the behavior of special students photographed later into the suggestion system and analyzing the results by comparing it with the existing data again. The test has been performed by arbitrarily applying unspecific behaviors that are not stored in the database, and the forecast model has accurately classified and grouped the input data. Also, it has been verified that it is possible to accurately distinguish and classify the behaviors through the feature data of the behaviors even if there are some errors in the input process.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.689-692
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• 2016

출입이 없는 폐쇄된 환경에서 객체의 자동 감시 시스템은 객체의 움직임을 추적하여 객체의 지속적인 관찰과 함께 행동 패턴을 효율적으로 분석하기 위해 사용되고 있다. 특히, 국내 돈사의 경우 돈사 내 여러 마리의 돼지들을 개별적으로 관리하기 위해 이러한 감시 시스템은 필수적이다. 그러나 여러마리의 돼지들이 근접하여 개별적으로 추적하기 위해서는 비디오 스트림에서의 매 프레임마다 정확한 분리가 되어야 한다. 본 논문에서는 근접한 돼지의 시퀀스에 분리 알고리즘을 이용하여 매 프레임마다 정확도를 측정한 후 통계적 검정을 통하여 근접한 객체에 대한 최적의 분리 알고리즘을 결정하는 방법을 제안한다. 즉, 시퀀스의 연속된 프레임에서 분리 정확도를 계산하고 통계적 가설 검정을 수행하여 분리 정확도가 일정 수치를 넘지 못하면 다른 분리 알고리즘을 수행하도록 결정한다. 실험 결과, 제안 방법을 이용하여 제안된 가설에 의해 매 프레임마다 최적의 분리 알고리즘을 수행하도록 결정하였다.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.550-558
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• 2015

This study investigates the degree of intention-behavior gap in the entertainment tourism. Using the sample of international visitors to South Korea, we identified the inclined actor (who are interested in the entertainment performance and actually went to the entertainment performance) and inclined abstainer (who are interested in the entertainment performance but did not go to the entertainment performance). The results of logistic regression analysis show that the sample was more accurately classified when attitude and knowledge on K-entertainment were included as explanatory variables. More findings and implications are provided.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.360-363
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• 2018

본 논문에서는 계층적 RNN의 성능 향상을 위하여 강화학습을 통한 계층적 RNN 내 파라미터를 효율적으로 찾는 방법을 제안한다. 계층적 RNN 내 임의의 파라미터에서 학습을 진행하고 얻는 분류 정확도를 보상으로 하여 간소화된 강화학습 네트워크에서 보상을 최대화하도록 강화학습 내부 파라미터를 수정한다. 기존의 강화학습을 통한 내부 구조를 찾는 네트워크는 많은 자원과 시간을 소모하므로 이를 해결하기 위해 간소화된 강화학습 구조를 적용하였고 이를 통해 적은 컴퓨터 자원에서 학습속도를 증가시킬 수 있었다. 간소화된 강화학습을 통해 계층적 RNN의 파라미터를 수정하고 이를 행동 인식 데이터 세트에 적용한 결과 기존 알고리즘 대비 높은 성능을 얻을 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.213-216
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• 2021

본 논문에서는 Text Classification에 사용된 딥러닝 모델을 적용하여 행동 인식, 손동작 인식 및 감정 인식 방법을 제안한다. 먼저 라이브러리를 사용하여 영상에서 특징 추출 후 식을 적용하여 특징의 벡터를 저장한다. 이를 Conv1D, Transformer, GRU를 결합한 모델에 학습시킨다. 이 방법을 통해 하나의 딥러닝 모델을 사용하여 다양한 분야에 적용할 수 있다. 제안한 방법을 사용해 SYSU 3D HOI 데이터셋에서 99.66%, eNTERFACE' 05 데이터셋에 대해 99.0%, DHG-14 데이터셋에 대해 95.48%의 클래스 분류 정확도를 얻을 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.7-9
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• 2020

기존 CCTV 비디오에서 딥러닝 기반의 이상 탐지 연구는 객체의 행동 값만을 이용하여 이상을 탐지하였기 때문에, 시간 흐름에 따른 정보가 축소되는 문제점이 있었다. 그러나 CCTV 비디오에서의 이상의 원인은 다양한 요소와 시계열 분석에 따른 정보로 이루어져 있어 시간 정보를 유지하면서 다양한 특징 값을 사용한 모델을 설계할 필요가 있다. 따라서 본 논문에서는 C3D에 광학 흐름을 결합한 새로운 앙상블 모델을 제안한다. 실험 결과 본 논문에서 제안하는 모델이 75.83의 AUC를 얻어 기존에 연구되었던 행동 값만을 사용한 모델보다 높은 정확도를 달성하였다. 또한 이상 탐지 모델 설계 시 객체의 행동에 다양한 측면을 고려할 수 있는 여러 특징 값과 시계열 분석에 따른 정보를 사용하는 것이 적절하다는 결론을 도출하였다.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.155-160
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• 2017

Recently, Deep learning has been used successfully to solve many recognition problems. It has many advantages over existing machine learning methods that extract feature points through hand-crafting. Deep neural networks for human activity recognition split video data into frame images, and then classify activities by analysing the connectivity of frame images according to the time. But it is difficult to apply to actual problems which has small-scale activity classes. Because this situations has a problem of overfitting and insufficient training data. In this paper, we defined 5 type of small-scale human activities, and classified them. We construct video database using 700 video clips, and obtained a classifying accuracy of 74.00%.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.187-194
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• 2021

A person's behavioral recognition is the recognition of what a person does according to joint movements. To this end, we utilize computer vision tasks that are utilized in image processing. Human behavior recognition is a safety accident response service that combines deep learning and CCTV, and can be applied within the safety management site. Existing studies are relatively lacking in behavioral recognition studies through human joint keypoint extraction by utilizing deep learning. There were also problems that were difficult to manage workers continuously and systematically at safety management sites. In this paper, to address these problems, we propose a method to recognize risk behavior using only joint keypoints and joint motion information. AlphaPose, one of the pose estimation methods, was used to extract joint keypoints in the body part. The extracted joint keypoints were sequentially entered into the Long Short-Term Memory (LSTM) model to be learned with continuous data. After checking the behavioral recognition accuracy, it was confirmed that the accuracy of the "Lying Down" behavioral recognition results was high.