• 한국콘텐츠학회논문지
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• 제13권4호
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• pp.64-72
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• 2013

본 연구에서는 다차원의 데이터 인식에 유리한 SVM을 이용한 동적 동작인식 알고리즘을 제안한다. 우선, Kinect 비디오 프레임에서 동작의 시작과 끝을 찾아 의미있는 동작 프레임을 분할하고, 프레임 수를 동일하게 정규화시킨다. 정규화된 프레임에서 인체 모델에 기반한 인체 부위의 위치와 부위 사이의 관계를 이용한 동작 특징을 추출하여 동작인식을 수행한다. 동작인식기인 C-SVM는 각 동작에 대해 positive 데이터와 negative 데이터로 구성된 학습 데이터로 학습된다. 최종 동작 선정은 각 C-SVM의 결과값 중 가장 큰 값을 갖는 동작으로 한다. 제안하는 동작인식 알고리즘은 플래시 구연동화에서 더 나아가 유아가 능동적으로 구연동화에 참여할 수 있도록 고안된 체감형 동화 콘텐츠에 동작 인터페이스로 적용되었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(3)
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• pp.121-124
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• 2000

In this paper, we propose a pen gesture recognition system for user interface in multimedia terminal which requires fast processing time and high recognition rate. It is realtime and interaction system between graphic and text module. Text editing in recognition system is performed by pen gesture in graphic module or direct editing in text module, and has all 14 editing functions. The pen gesture recognition is performed by searching classification features that extracted from input strokes at pen gesture model. The pen gesture model has been constructed by classification features, ie, cross number, direction change, direction code number, position relation, distance ratio information about defined 15 types. The proposed recognition system has obtained 98% correct recognition rate and 30msec average processing time in a recognition experiment.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권1호
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• pp.22-31
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• 2004

This paper deals with the dynamic hand gesture recognition based on computer vision using neural networks. This paper proposes a global search method and a local search method to recognize the hand gesture. The global search recognizes a hand among the hand candidates through the entire image search, and the local search recognizes and tracks only the hand through the block search. Dynamic hand gesture recognition method is based on the skin-color and shape analysis with the invariant moment and direction information. Starting point and ending point of the dynamic hand gesture are obtained from hand shape. Experiments have been conducted for hand extraction, hand recognition and dynamic hand gesture recognition. Experimental results show the validity of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.6063-6079
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• 2019

When the length of sampling data sequence is too large, the method of gesture recognition based on traditional Dynamic Time Warping (DTW) algorithm will lead to too long calculation time, and the accuracy of recognition result is not high.Support vector machine (SVM) has some shortcomings in precision, Edit Distance on Real Sequences(EDR) algorithm does not guarantee that noise suppression will not suppress effective data.A new method based on Improved Interpolation Dynamic Time Warping (IIDTW)algorithm is proposed to improve the efficiency of gesture recognition and the accuracy of gesture recognition. The results show that the computational efficiency of IIDTW algorithm is more than twice that of SVM-DTW algorithm, the error acceptance rate is FAR reduced by 0.01%, and the error rejection rate FRR is reduced by 0.5%.Gesture recognition based on IIDTW algorithm can achieve better recognition status. If it is applied to unlock mobile phone, it is expected to become a new generation of unlock mode.

• 반도체디스플레이기술학회지
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• 제19권3호
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• pp.106-111
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• 2020

In this paper, we propose the non-contact gesture recognition system and algorithm using proximity-based sensors. The system uses four IR receiving photodiode embedded on a single chip and an IR LED for small area. The goal of this paper is to use the proposed algorithm to solve the problem associated with bringing the four IR receivers close to each other and to implement a gesture sensor capable of recognizing eight directional gestures from a distance of 10cm and above. The proposed system was implemented on a FPGA board using Verilog HDL with Android host board. As a result of the implementation, a 2-D swipe gesture of fingers and palms of 3cm and 15cm width was recognized, and a recognition rate of more than 97% was achieved under various conditions. The proposed system is a low-power and non-contact HMI system that recognizes a simple but accurate motion. It can be used as an auxiliary interface to use simple functions such as calls, music, and games for portable devices using batteries.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.995-998
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• 2002

In this paper, we present a fast algorithm for hand gesture recognition of a human from an image by using the directivity information of the fingers. To implement a fast recognition system, we applied the morphological shape decomposition. A proposed gesture recognition algorithm has been tested on the 300 ${\times}$ 256 digital images. Our experiments using image acquired image camera have shown that the proposed hand gesture recognition algorithm is effective.

• 로봇학회논문지
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• 제3권2호
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• pp.81-90
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• 2008

While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in $KAIST^[1]$. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user's hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.

• 한국멀티미디어학회논문지
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• 제16권12호
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• pp.1393-1402
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• 2013

Hand gesture is one of the most popular communication methods in everyday life. In human-computer interaction applications, hand gesture recognition provides a natural way of communication between humans and computers. There are mainly two methods of hand gesture recognition: glove-based method and vision-based method. In this paper, we propose a vision-based hand gesture recognition method using Kinect. By using the depth information is efficient and robust to achieve the hand detection process. The finger labeling makes the system achieve pose classification according to the finger name and the relationship between each fingers. It also make the classification more effective and accutate. Two kinds of gesture sets can be recognized by our system. According to the experiment, the average accuracy of American Sign Language(ASL) number gesture set is 94.33%, and that of general gestures set is 95.01%. Since our system runs in real-time and has a high recognition rate, we can embed it into various applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2824-2838
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• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

• 한국통신학회논문지
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• 제33권5C호
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• pp.371-377
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• 2008

본 논문은 비전에 기반한 사람의 양팔 제스처의 모델링과 인식에 관한 연구이다. 우리는 양팔 제스처 인식을 위한 특징점의 추출에서부터 제스처의 분류에 이르는 전체적 틀을 제안하였다. 먼저, 양팔 제스처의 모델링을 위해 색채 기반의 양손 추적 방법을 제안하였고, 추출된 양손의 궤적 정보를 효과적으로 선택하게 하는 제스처 구(Phrase) 분석법을 제시하였다. 선택된 특징 점들의 시퀀스(sequence) 들로 이루어진 훈련 데이터들의 최대 공통부열(Longest Common Subsequence) 정보를 이용하여 제스처를 모델링하고 이에 따른 유사도 척도를 제안하였다. 제안된 방법론을 공항 등에서 이용하는 항공기 유도 수신호에 적용하였고, 실험을 통해 제안된 방법론의 효율성과 인식성능을 보였다.