Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67893
Title: | 卷積類神經網路結合高斯混合模型的背景消去法 Convolutional Neural Networks for Background Subtraction with Gaussian mixture background models |
Authors: | Ting-Yuan Lin 林定遠 |
Advisor: | 莊永裕(Yung-Yu Chuang) |
Keyword: | 背景消去,卷積類神經網路,高斯混合模型,時序中值濾波, background subtraction,convolutional Neural Networks,Gaussian mixture models,temporal median filter, |
Publication Year : | 2017 |
Degree: | 碩士 |
Abstract: | 此篇論文探討背景消去演算法,不同於傳統的作法,我們將目標影像與背景影像丟給卷積類神經網路來做訓練。重點在於背景影像的產生,背景影像並非時序的中值濾波所產生的,而是由高斯混合模型所產生,在此基礎下,背景的可信度將有卓越的提升。而我們也探討灰階影像與彩色影像對訓練結果的影響,以及是否讓卷積類神經網路產生的前景遮罩參與高斯混合模型的背景生成。多方探討下,我們發現在選取的2014 ChangeDetection.net資料庫中,展現出良好的結果,優於當前的IUTIS-5、PAWCS、SuBSENSE等方法。 This paper aims to analyze background subtraction algorithm. Different from tradition methods, we feed the trained network with the target and background images. Focusing on how to get the background images. Not using the temporal median filter. We use the Gaussian mixture models to produce background images. In this way, the accuracy of background images increases. We also research the difference between grayscale and RGB images. And whether adding the foreground masks from the convolutional Neural Networks to the Gaussian mixture models or not. Experiments lead on 2014 ChangeDetection.net dataset show that our proposed method outperforms several state-of-the-art methods, including IUTIS-5, PAWCS, SuBSENSE and so on. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67893 |
DOI: | 10.6342/NTU201701837 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 資訊工程學系 |
Files in This Item:
File | Size | Format | |
---|---|---|---|
ntu-106-1.pdf Restricted Access | 1.17 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.