• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.468-471
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• 2008

이 논문은 응급상황을 인식하기 위하여 어안렌즈를 통해 획득된 영상을 이용하여 기절 동작을 인식하는 방법을 제안한다. 거실의 천장 중앙에 위치한 어안렌즈(fish-eye lens)를 장착한 카메라로부터 화각이 170인 RGB 컬러 모델의 어안 영상을 입력 받은 뒤, 가우시안 혼합 모델 기반의 적응적 배경 모델링 방법을 이용하여 동적으로 배경 영상을 갱신한다. 입력 영상의 평균 밝기를 구하고 평균 밝기가 급격하게 변화하지 않도록 영상 픽셀을 보정한 뒤, 입력 영상과 배경 영상과 차이가 큰 픽셀을 찾음으로써 움직이는 객체를 추출하였다. 그리고 연결되어 있는 전경 픽셀 영역들의 외곽점들을 추적하여 타원으로 매핑하고 움직이는 객체 영역의 형태를 단순화하였다. 이 타원을 추적하면서 어안 렌즈 영상을 투시 영상으로 변환한 다음 타원의 크기 변화, 위치 변화, 이동 속도 정보를 추출하여 이동과 정지 및 움직임이 기절동작과 유사한지를 판단하도록 하였다. 본 논문에서는 실험자로 하여금 기절동작, 걷기 동작, 앉기 동작 등 여러 동작을 취하게 하고 기절 동작 인식을 실험하였다. 실험 결과 어안 렌즈 영상을 그대로 사용하는 것보다 투시 영상으로 변환하여 타원의 크기변화, 위치변화, 이동속도 정보를 이용하는 것이 높은 인식률을 보였다.

• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.225-236
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• 2010

This study proposes a method for the recognition of emergency situation according to the bimodal information of camera image sensor and gravity sensor. This method can recognize emergency condition by mutual cooperation and compensation between sensors even when one of the sensors malfunction, the user does not carry gravity sensor, or in the place like bathroom where it is hard to acquire camera images. This paper implemented HMM(Hidden Markov Model) based learning and recognition algorithm to recognize actions such as walking, sitting on floor, sitting at sofa, lying and fainting motions. Recognition rate was enhanced when image feature vectors and gravity feature vectors are combined in learning and recognition process. Also, this method maintains high recognition rate by detecting moving object through adaptive background model even in various illumination changes.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.181-190
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• 2009

This paper presents a multimodal emergency recognition system based on visual information, audio information and gravity sensor information. It consists of video processing module, audio processing module, gravity sensor processing module and multimodal integration module. The video processing module and gravity sensor processing module respectively detects actions such as moving, stopping and fainting and transfer them to the multimodal integration module. The multimodal integration module detects emergency by fusing the transferred information and verifies it by asking a question and recognizing the answer via audio channel. The experiment results show that the recognition rate of video processing module only is 91.5% and that of gravity sensor processing module only is 94%, but when both information are combined the recognition result becomes 100%.

• Journal of the Korea Society of Computer and Information
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• v.13 no.4
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• pp.97-103
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• 2008

This study is to present a fainting motion recognizing method by using fish-eye lens images to sense emergency situations. The camera with fish-eye lens located at the center of the ceiling of the living room sends images, and then the foreground pixels are extracted by means of the adaptive background modeling method based on the Gaussian complex model, which is followed by tracing of outer points in the foreground pixel area and the elliptical mapping. During the elliptical tracing, the fish-eye lens images are converted to fluoroscope images. the size and location changes, and moving speed information are extracted to judge whether the movement, pause, and motion are similar to fainting motion. The results show that compared to using fish-eye lens image, extraction of the size and location changes. and moving speed by means of the conversed fluoroscope images has good recognition rates.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.279-287
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• 2006

This paper proposes a method to detect fall action by using stereo images to recognize emergency situation. It uses 3D information to extract the visual information for learning and testing. It uses HMM(Hidden Markov Model) as a recognition algorithm. The proposed system extracts background images from two camera images. It extracts a moving object from input video sequence by using the difference between input image and background image. After that, it finds the bounding rectangle of the moving object and extracts 3D information by using calibration data of the two cameras. We experimented to the recognition rate of fall action with the variation of rectangle width and height and that of 3D location of the rectangle center point. Experimental results show that the variation of 3D location of the center point achieves the higher recognition rate than the variation of width and height.