Adaptive Background Dengan Metode Gaussian Mixture Models Untuk Real-Time Tracking

Rostianingsih, Silvia; Adipranata, Rudy; Wibisono, Fredy Setiawan

doi:10.9744/informatika.9.1.68-77

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…The background is an image in which there are objects that are not moving while an image that contains moving objects is called the foreground. In a video, the background and foreground are always http://dx.doi.org/10.35671/telematika.v15i1.1222 changing, so we always have to update the background on each frame (Rostianingsih et al, 2008).…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

Detection and Classification of Vehicles on the Bekasi Toll Road Using the Gaussian Mixture Models Method and Morphological Operations

Kosasih

2022

telematika.

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Traffic surveillance was initially carried out directly using CCTV, but this kind of surveillance was not possible for a full day by the security forces. In addition, with the increasing growth of vehicles in Indonesia, a method is needed that can be used to assist security forces in monitoring traffic such as detecting and automatically counting the number of vehicles. Therefore, in our research, we propose a method that can detect vehicles, and count the number of vehicles from video recordings on the Bintara Bekasi toll road using background substraction methods such as gaussian mixture models and morphological operations. The results showed that the vehicle detection accuracy rate was 86.3636%, the precision was 89.0625%, and the recall was 96.6101%. In this study, vehicle classification was also carried out based on the detection results into two types of vehicles, namely cars and trucks. From the results of the research, the classification accuracy rate was obtained at 85.9649%.

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Section: Gaussian Mixture Modelmentioning

confidence: 99%

Detection and Classification of Vehicles on the Bekasi Toll Road Using the Gaussian Mixture Models Method and Morphological Operations

Kosasih

2022

telematika.

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“…The background is another object that does not belong to the object of observation. In another word, The background is the static object that does not change frequently and foreground is dynamic object that moves from one point to another in certain sequential frames [8].…”

Section: Background Extractionmentioning

confidence: 99%

Adaptive Background Extraction for Video Based Traffic Counter Application Using Gaussian Mixture Models Algorithm

2015

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“…To make predictions more accurate, we use the Gaussian Mixture Model (GMM) [4,5] to decompose stock price series into the terms of trend. Then introduce these terms by combining the technical indicators data into the SVM to make price predictions.…”

Section: Introductionmentioning

confidence: 99%

Indonesian Stock Prediction using Support Vector Machine (SVM)

2018

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Abstract. This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence) or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK) stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data) and the remainder (20 %) was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

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Adaptive Background Dengan Metode Gaussian Mixture Models Untuk Real-Time Tracking

Cited by 6 publications

References 2 publications

Detection and Classification of Vehicles on the Bekasi Toll Road Using the Gaussian Mixture Models Method and Morphological Operations

Detection and Classification of Vehicles on the Bekasi Toll Road Using the Gaussian Mixture Models Method and Morphological Operations

Adaptive Background Extraction for Video Based Traffic Counter Application Using Gaussian Mixture Models Algorithm

Indonesian Stock Prediction using Support Vector Machine (SVM)

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