• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.844-846
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• 2005

본 논문은 시점에 독립적인 제스처 인식을 위하여 볼륨 모션 템플릿을 제안한다. 기존 제스처 연구에서 시점 문제와 행동 속도의 편차는 중요하면서도 어려운 문제이다. 첫째, 시점 문제는 하나의 단안 카메라나 스테레오 카메라를 이용하는 단방향 카메라 환경에서 발생하며 해결하기 어려운 문제이다. 모든 시점에서 학습시켜야 하는 기존 연구의 단점을 해결하기 위해, 다양한 시점입력에 독립적으로 인식을 할 수 있는 볼륨 모션 템플릿을 제안한다. 볼륨 모션 템플릿은 깊이 정보와 모션의 방향성 통해 최적의 가상 시점을 제공한다. 또한 볼륨 모션 템플릿을 이용하여 시스템의 신뢰성과 확장성 또한 개선하였다. 두 번째, 제스처가 발생 시마다 생기는 속도의 편차 문제이다. 입력 제스처의 시간-정규화를 통해 해결할 수 있는데, 시간 정보 대신 모션 량을 사용하여 이를 해결하였다. 볼륨 모션 템플릿을 이용하여 다양한 시점 입력에 대해 실험하였고, 기존 모션 히스토리 이미지와 비교하여 시점에 독립적인 결과를 얻었다.

• Annual Conference of KIPS
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• 2020.05a
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• pp.633-634
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• 2020

본 논문에서는 미디어영상을 기반한 무대 공연의 다양한 미디어 효과를 분석하고, 무대 공연을 위한 제스처 기반 동적 프로젝션 맵핑 프레임워크를 설계 구현한다. 이를 위하여, 동적 프로젝션 맵핑 기반 기존 공연에서 공연자의 제스처와 이에 따른 미디어 효과를 분석하고, 동적 프로젝션 맵핑기술을 효율적으로 구현하기 위하여 모션 히스토리 이미지를 이용한 CNN(Convolutional Neural Network) 기반의 제스처 인식 기술을 구현한다. 또한, 구현된 제스처인식 기술을 기반으로 공연자의 서로 다른 제스처와 미디어 효과를 매칭시킬 수 있는 프레임 워크 구현 내용을 소개한다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.1-13
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• 2011

In this paper, we propose a gesture recognition system to recognize motions using the shape information of MHI (Motion History Image). The system acquires MHI to provide information on motions from images with input and extracts the gradient images from such MHI for each X and Y coordinate. It extracts the shape information by applying the shape context to each gradient image and uses the extracted pattern information values as the feature values. It recognizes motions by learning and classifying the obtained feature values with a SVM (Support Vector Machine) classifier. The suggested system is able to recognize the motions for multiple people as well as to recognize the direction of movements by using the shape information of MHI. In addition, it shows a high ratio of recognition with a simple method to extract features.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1828-1836
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• 2015

Object tracking algorithms can reduce computational cost by avoiding computation over the whole image through the selection of region of interests based on object detection. So, accurate object detection is an important task for object tracking. The background subtraction algorithm has been widely used in moving object detection using a stationary camera. However, it has the problem of object detection error due to incorrect background modeling, whereas the method of background modeling has been improved by many researches. This paper proposes a new moving object detection algorithm to overcome the drawback of the conventional background subtraction algorithm by combining the background subtraction algorithm with the motion history image algorithm that is usually used in gesture detection. Although the proposed algorithm demands more processing time because of time taken for combining two algorithms, it meet the real-time processing requirement. Moreover, experimental results show that it has higher accuracy compared with the previous two algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2291-2297
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• 2015

This paper presents a vehicle detection system using the video as a input image what has moving of vehicles.. Input image has constraints. it has to get fixed view and downward view obliquely from top of the road. Road detection is required to use only the road area in the input image. In introduction, we suggest the experiment result and the critical point of motion history image extraction method, SIFT(Scale_Invariant Feature Transform) algorithm and histogram analysis to detect vehicles. To solve these problem, we propose using applied Gaussian Mixture Model(GMM) that is the Vehicle Detection GMM(VDGMM). In addition, we optimize VDGMM to detect vehicles more and named VDGMM 2.0. In result of experiment, each precision, recall and F1 rate is 9%, 53%, 15% for GMM without road detection and 85%, 77%, 80% for VDGMM2.0 with road detection.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.545-559
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• 2020

In this paper, we propose a content creation framework that enables users without programming experience to easily create interactive media content that responds to user gestures. In the proposed framework, users define the gestures they use and the media effects that respond to them by numbers, and link them in a text-based configuration file. In the proposed framework, the interactive media content that responds to the user's gesture is linked with the dynamic projection mapping module to track the user's location and project the media effects onto the user. To reduce the processing speed and memory burden of the gesture recognition, the user's movement is expressed as a gray scale motion history image. We designed a convolutional neural network model for gesture recognition using motion history images as input data. The number of network layers and hyperparameters of the convolutional neural network model were determined through experiments that recognize five gestures, and applied to the proposed framework. In the gesture recognition experiment, we obtained a recognition accuracy of 97.96% and a processing speed of 12.04 FPS. In the experiment connected with the three media effects, we confirmed that the intended media effect was appropriately displayed in real-time according to the user's gesture.