• Title/Summary/Keyword: Gaussian mixture model-based

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Measuring of Effectiveness of Tracking Based Accident Detection Algorithm Using Gaussian Mixture Model (가우시안 배경혼합모델을 이용한 Tracking기반 사고검지 알고리즘의 적용 및 평가)

  • Oh, Ju-Taek;Min, Jun-Young
    • International Journal of Highway Engineering
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    • v.14 no.3
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    • pp.77-85
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    • 2012
  • Most of Automatic Accident Detection Algorithm has a problem of detecting an accident as traffic congestion. Actually, center's managers deal with accidents depend on watching CCTV or accident report by drivers even though they run the Automatic Accident Detection system. It is because of the system's detecting errors such as detecting non-accidents as accidents, and it makes decreasing in the system's overall reliability. It means that Automatic Accident Detection Algorithm should not only have high detection probability but also have low false alarm probability, and it has to detect accurate accident spot. The study tries to verify and evaluate the effectiveness of using Gaussian Mixture Model and individual vehicle tracking to adapt Accident Detection Algorithm to Center Management System by measuring accident detection probability and false alarm probability's frequency in the real accident.

Application of Gaussian Mixture Model for the Analysis of the Nanoindentation Test Results of the Metakaolin-based Geopolymer with Different Silicon-to-Aluminum Molar Ratio (실리콘-알루미늄 몰 비의 변화에 따른 메타카올린 지오폴리머의 나노인덴테이션 결과 분석을 위한 가우시안 믹스쳐 모델의 활용)

  • Park, Sungwoo
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.35 no.2
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    • pp.101-107
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    • 2022
  • This study proposes the deconvolution method for the nanoindentation test results of geopolymer employing the Gaussian mixture model. Geopolymer has been studied extensively as an alternative construction material because it emits relatively lower CO2 compared to ordinary Portland cement. Geopolymer is made of aluminosilicate and alkaline solution, and the Si/Al molar ratio affects its mechanical properties. Previous studies revealed that the Si/Al molar ratio of 1.8~2.0 results in the highest compressive strength, and the Si/Al molar ratio over 1.8 degrades the compressive strength of geopolymer severely; however the reason for the compressive strength degradation is still unclear. To understand the effect of the Si/Al molar ratio on the geopolymer structure, this study exploits the nanoindentation. The phase deconvolution of the indent modulus data is successful using the Gaussian mixture model, and it is observed that the Si/Al molar ratio alters the homogeneity of the geopolymer. Geopolymer becomes more homogeneous up to an Si/Al molar ratio of 1.8 at which geopolymer exhibits the highest compressive strength. The examination of this study is assumed to be adopted as evidence of strength degradation by the Si/Al ratio higher than the optimum value.

Improvement of Speech Reconstructed from MFCC Using GMM (GMM을 이용한 MFCC로부터 복원된 음성의 개선)

  • Choi, Won-Young;Choi, Mu-Yeol;Kim, Hyung-Soon
    • MALSORI
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    • no.53
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    • pp.129-141
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    • 2005
  • The goal of this research is to improve the quality of reconstructed speech in the Distributed Speech Recognition (DSR) system. For the extended DSR, we estimate the variable Maximum Voiced Frequency (MVF) from Mel-Frequency Cepstral Coefficient (MFCC) based on Gaussian Mixture Model (GMM), to implement realistic harmonic plus noise model for the excitation signal. For the standard DSR, we also make the voiced/unvoiced decision from MFCC based on GMM because the pitch information is not available in that case. The perceptual test reveals that speech reconstructed by the proposed method is preferred to the one by the conventional methods.

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Video Based Fire Detection Algorithm using Gaussian Mixture Model (Gaussian 혼합모델을 이용한 영상기반 화재검출 알고리즘)

  • Park, Jang-Sik;Kim, Hyun-Tae;Yu, Yun-Sik
    • The Journal of the Korea institute of electronic communication sciences
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    • v.6 no.2
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    • pp.206-211
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    • 2011
  • In this paper, a fire detection algorithm based on video processing is proposed. At the first stage, background image extracted from CCTV video input signal, and then foreground image were separated by differencing CCTV input signal from background image. At the second stage, candidated area were extracted by using color information from foreground image. At the final stage, smoke or flame characteristic area were separated by using Gaussian mixture modeling applied to candidated area, and then fire can be detected. Through real experiments at the inner room, it is shown that the proposed system works well.

Layered Object Detection using Adaptive Gaussian Mixture Model in the Complex and Dynamic Environment (혼잡한 환경에서 적응적 가우시안 혼합 모델을 이용한 계층적 객체 검출)

  • Lee, Jin-Hyung;Cho, Seong-Won;Kim, Jae-Min;Chung, Sun-Tae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.3
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    • pp.387-391
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    • 2008
  • For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

Development of the Algofithm for Gaussian Mixture Models based Traffic Accident Auto-Detection in Freeway (GMM(Gaussian Mixture Model)을 적용한 영상처리기법의 연속류도로 사고 자동검지 알고리즘 개발)

  • O, Ju-Taek;Im, Jae-Geuk;Yeo, Tae-Dong
    • Journal of Korean Society of Transportation
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    • v.28 no.3
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    • pp.169-183
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    • 2010
  • Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a freeway and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, existing technologies, this freeway attribute, velocity changes, volume changes, occupancy changes reflect judge the primary. Furthermore, We pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian Mixture model analytical method which has been considered the best among well-known environmental obstacle reduction methods. Therefore, in this way, the accident was the final decision. Also, environmental factors occur frequently, and with the index finger situations, effectively reducing that can actively and environmentally adaptive techniques through accident final judgment. This implementation of the evaluate performance of the experiment road of 12 incidents in simulated and the jang-hang IC's real-time accident experiment. As a result, the do well detection 93.33%, false alarm 6.7% as showed high reliability.

Machine Learning Model for Low Frequency Noise and Bias Temperature Instability (저주파 노이즈와 BTI의 머신 러닝 모델)

  • Kim, Yongwoo;Lee, Jonghwan
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.4
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    • pp.88-93
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    • 2020
  • Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.

A vision based people tracking and following for mobile robots using CAMSHIFT and KLT feature tracker (캠시프트와 KLT특징 추적 알고리즘을 융합한 모바일 로봇의 영상기반 사람추적 및 추종)

  • Lee, S.J.;Won, Mooncheol
    • Journal of Korea Multimedia Society
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    • v.17 no.7
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    • pp.787-796
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    • 2014
  • Many mobile robot navigation methods utilize laser scanners, ultrasonic sensors, vision camera, and so on for detecting obstacles and path following. However, human utilizes only vision(e.g. eye) information for navigation. In this paper, we study a mobile robot control method based on only the camera vision. The Gaussian Mixture Model and a shadow removal technology are used to divide the foreground and the background from the camera image. The mobile robot uses a combined CAMSHIFT and KLT feature tracker algorithms based on the information of the foreground to follow a person. The algorithm is verified by experiments where a person is tracked and followed by a robot in a hallway.

Detection and Recognition of Illegally Parked Vehicles Based on an Adaptive Gaussian Mixture Model and a Seed Fill Algorithm

  • Sarker, Md. Mostafa Kamal;Weihua, Cai;Song, Moon Kyou
    • Journal of information and communication convergence engineering
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    • v.13 no.3
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    • pp.197-204
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    • 2015
  • In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussian mixture model (GMM) is used for background subtraction in a complex environment to identify the regions of moving objects in our test video. Stationary objects are detected by using the pixel-level features in time sequences. A stationary vehicle is detected by using the local features of the object, and thus, information about illegally parked vehicles is successfully obtained. An automatic alarm system can be utilized according to the different regulations of different illegal parking regions. The results of this study obtained using a test video sequence of a real-time traffic scene show that the proposed method is effective.

Global Covariance based Principal Component Analysis for Speaker Identification (화자식별을 위한 전역 공분산에 기반한 주성분분석)

  • Seo, Chang-Woo;Lim, Young-Hwan
    • Phonetics and Speech Sciences
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    • v.1 no.1
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    • pp.69-73
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    • 2009
  • This paper proposes an efficient global covariance-based principal component analysis (GCPCA) for speaker identification. Principal component analysis (PCA) is a feature extraction method which reduces the dimension of the feature vectors and the correlation among the feature vectors by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when performing PCA to find the eigenvalue and eigenvector matrix using the full covariance matrix by each speaker. The proposed method first calculates the global covariance matrix using training data of all speakers. It then finds the eigenvalue matrix and the corresponding eigenvector matrix from the global covariance matrix. Compared to conventional PCA and Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity in speaker identification.

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