• 한국지능시스템학회논문지
• /
• 제25권3호
• /
• pp.272-278
• /
• 2015

본 논문에서는 음성신호 피치 정보를 이용한 감정 인식 시스템 개발을 목표로 피치 정보로부터 다양한 파라메터 추출방법을 연구하였다. 이를 위하여 다양한 감정이 포함된 한국어 음성 데이터베이스를 이용하여 피치의 통계적인 정보와 수치해석 기법을 사용한 피치 파라메터를 생성하였다. 이러한 파라메터들은 GMM(Gaussian Mixture Model) 기반의 감정 인식 시스템을 구현하여 각 파라메터의 성능을 비교되었다. 또한 순차특징선택 방법을 사용하여 최고의 감정 인식 성능을 나타내는 피치 파라메터들을 선정하였다. 4개의 감정을 구별하는 실험 결과에서 총 56개의 파라메터중에서 15개를 조합하였을 때 63.5%의 인식 성능을 나타내었다. 또한 감정 검출 여부를 나타내는 실험에서는 14개의 파라메터를 조합하였을 때 80.3%의 인식 성능을 나타내었다.

• 말소리와 음성과학
• /
• 제1권4호
• /
• pp.127-132
• /
• 2009

The feature vectors which are used in conventional speaker recognition (SR) systems may have many correlations between their neighbors. To improve the performance of the SR, many researchers adopted linear transformation method like principal component analysis (PCA). In general, the linear transformation of the feature vectors is based on concatenated form of the static features and their dynamic features. However, the linear transformation which based on both the static features and their dynamic features is more complex than that based on the static features alone due to the high order of the features. To overcome these problems, we propose an efficient method that applies linear transformation and temporal information of the features to reduce complexity and improve the performance in speaker verification (SV). The proposed method first performs a linear transformation by PCA coefficients. The delta parameters for temporal information are then obtained from the transformed features. The proposed method only requires 1/4 in the size of the covariance matrix compared with adding the static and their dynamic features for PCA coefficients. Also, the delta parameters are extracted from the linearly transformed features after the reduction of dimension in the static features. Compared with the PCA and conventional methods in terms of equal error rate (EER) in SV, the proposed method shows better performance while requiring less storage space and complexity.

• 한국전자통신학회논문지
• /
• 제13권3호
• /
• pp.571-578
• /
• 2018

최근 딥러닝 기법이 다양한 종류의 패턴 인식에 있어서 우수한 성능을 보이고 있다. 하지만 소규모의 훈련데이터를 이용한 분류 실험에 있어서 전통적으로 사용되던 머신러닝 기법에 비해서 DNN의 성능이 우수한지에 대해서는 다소 간의 논란이 있어 왔다. 본 연구에서는 오디오 검출에 있어서 전통적으로 사용되어 왔던 GMM, SVM의 성능과 DNN의 성능을 비교하였다. 동일한 데이터에 대해서 인식실험을 수행한 결과, 전반적인 성능은 DNN이 우수하였으나 세그먼트 기반의 F-score에서 SVM이 DNN에 비해 우수한 성능을 보임을 알 수 있었다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
• /
• pp.268-269
• /
• 2008

Moving object detection is a fundamental technique in visual surveillance. Robust technique to enhance performance of moving object detection is required for several bad conditions in real external circumtance. In case of sudden illumination change in outdoor condition, many objects are determined as moving object though they are not really moving, but just their illumination changes. This makes the detection result untrustworthy. In this paper, robust moving object detection to sudden illumination change using gaussian mixture background model and new texture information using background from the weighted sum of recent images is proposed.

• 전기학회논문지
• /
• 제66권10호
• /
• pp.1516-1522
• /
• 2017

We propose a novel monitoring system for diagnosing crack faults of the wind turbine using image information. The proposed method classifies a normal state and a abnormal state for the blade parts of the wind turbine. Specifically, the images are input to the proposed system in various states of wind turbine rotation. according to the blade condition. Then, the video of rotating blades on the wind turbine is divided into several image frames. Motion vectors are estimated using the previous and current images using the motion estimation, and the change of the motion vectors is analyzed according to the blade state. Finally, we determine the final blade state using the Support Vector Machine (SVM) classifier. In SVM, features are constructed using the area information of the blades and the motion vector values. The experimental results showed that the proposed method had high classification performance and its $F_1$ score was 0.9790.

• 한국멀티미디어학회논문지
• /
• 제18권11호
• /
• pp.1289-1301
• /
• 2015

Pedestrian detection (PD) is an essential task in various applications and sliding window-based methods utilizing HOG (Histogram of Oriented Gradients) or HOG-like descriptors have been shown to be very effective for accurate PD. However, due to exhaustive search across images, PD methods based on sliding window usually require heavy computational time. In this paper, we propose a real-time PD method for embedded visual surveillance with fixed backgrounds. The proposed PD method employs HOG descriptors as many PD methods does, but utilizes selective search so that it can save processing time significantly. The proposed selective search is guided by restricting searching to candidate regions extracted from Adaptive Gaussian Mixture Model (AGMM)-based background subtraction technique. Moreover, approximate computation of HOG descriptor and implementation in fixed-point arithmetic mode contributes to reduction of processing time further. Possible accuracy degradation due to approximate computation is compensated by applying an appropriate one among three offline trained SVM classifiers according to sizes of candidate regions. The experimental results show that the proposed PD method significantly improves processing speed without noticeable accuracy degradation compared to the original HOG-based PD and HOG with cascade SVM so that it is a suitable real-time PD implementation for embedded surveillance systems.

• 한국멀티미디어학회논문지
• /
• 제15권4호
• /
• pp.449-471
• /
• 2012

In intelligent surveillance systems, it is required to robustly track multiple people. Most of the previous studies adopted a Gaussian mixture model (GMM) for discriminating the object from the background. However, it has a weakness that its performance is affected by illumination variations and shadow regions can be merged with the object. And when two foreground objects overlap, the GMM method cannot correctly discriminate the occluded regions. To overcome these problems, we propose a new method of tracking and identifying multiple people. The proposed research is novel in the following three ways compared to previous research: First, the illuminative variations and shadow regions are reduced by an illumination normalization based on the median and inverse filtering of the L*a*b* image. Second, the multiple occluded and overlapped people are tracked by combining the GMM in the still image and the Lucas-Kanade-Tomasi (LKT) method in successive images. Third, with the proposed human tracking and the existing face detection & recognition methods, the tracked multiple people are successfully identified. The experimental results show that the proposed method could track and recognize multiple people with accuracy.

• 한국항행학회논문지
• /
• 제17권1호
• /
• pp.90-97
• /
• 2013

최근 영상처리 기법에 기반한 지능형 교통시스템의 개발이 활성화되고 있다. 본 논문에서는 도심 사거리에서 획득한 비디오를 분석하여 차량의 움직임 변화와 체류시간에 기반한 추돌 검출 알고리즘을 제안한다. 가우시안 혼합 모델 기반으로 생성된 배경과 입력영상의 차 영상으로부터 관심영역(ROI)안의 객체를 추출한다. 추출된 객체에 대해 계산된 움직임벡터와 화면 내 차량 체류시간을 이용하여 교차로 내 차량추돌과 교통체증을 검출하였다. 제안된 알고리즘은 추돌을 포함한 실제 교차로 영상에 대해 테스트되었고, 탐지율은 85.7%이고, 오탐율은 7.7%였다.

• 한국콘텐츠학회논문지
• /
• 제17권1호
• /
• pp.137-144
• /
• 2017

얼굴 검출은 복잡한 배경 내에서 다양한 얼굴의 자세로 인해 여전히 어려운 문제에 직면하고 있다. 본 논문은 피부색과 깊이 정보를 기반으로 한 한명 또는 여러 명의 얼굴을 검출하는 효과적인 알고리즘을 제안한다. 먼저 우리는 컬러 영상에서 가우시안 혼합 모델을 이용한 피부색 검출 방법에 대해 소개한다. 그리고 Kinect V2의 깊이 센서를 이용하여 획득한 3차원의 깊이 정보는 배경으로부터 사람의 몸을 분할할 때 유용하다. 그리고 레이블링 과정에서 여러 개의 특징을 이용하여 얼굴이 아닌 영역은 성공적으로 제거된다. 실험 결과를 통해 제안한 얼굴 검출 알고리즘은 다양한 조건과 복잡한 배경에서 얼굴이 효과적으로 검출되는 것을 확인할 수 있다.

• 한국콘텐츠학회:학술대회논문집
• /
• 한국콘텐츠학회 2009년도 춘계 종합학술대회 논문집
• /
• pp.889-894
• /
• 2009

Accompanied by the rapid development of Computer Vision, Visual surveillance has achieved great evolution with more and more complicated processing. However there are still many problems to be resolved for robust and reliable visual surveillance, and the cast shadow occurring in motion detection process is one of them. Shadow pixels are often misclassified as object pixels so that they cause errors in localization, segmentation, tracking and classification of objects. This paper proposes a novel cast shadow removal method. As opposed to previous conventional methods, which considers pixel properties like intensity properties, color distortion, HSV color system, and etc., the proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the background scene. Then, the product of the outcomes of application determines whether the blob pixels in the foreground mask comes from object blob regions or shadow regions. The proposed method is simple but turns out practically very effective for Gaussian Mixture Model, which is verified through experiments.