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研究生:孔維顥
研究生(外文):Wei-hao Kung
論文名稱:受雨滴影響之監控影片車流估測
論文名稱(外文):Traffic Flow Estimation in Rain-Drop tampered Surveillance Videos
指導教授:鄭旭詠
指導教授(外文):Hsu-yung Cheng
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:75
中文關鍵詞:車流估測受雨滴影響鏡頭
外文關鍵詞:Traffic Flow EstimationRaindrop-tampered camera
相關次數:
  • 被引用被引用:0
  • 點閱點閱:131
  • 評分評分:
  • 下載下載:2
  • 收藏至我的研究室書目清單書目收藏:0
在本篇論文中,我們提出了一個更為靈活的車流估測方法,然而利用國道影像資料分析路段車流,已有許多人付諸實行,無論是在白天或是夜間,都有許多技術的支持,即使是在天氣狀況不佳,視野不明的情況下,如:雨霧干擾。也有許多學者致力於去除雜訊,還原影像內容。而在這麼多的先進論文之後,我們將這篇論文的主要目標訂在找出一個解決方案,其即使攝影鏡頭受到雨水的嚴重影響也能夠穩定的分析出其中含有的車流,並且能有著高度靈活度的分析系統,其能夠盡量減少人力干預,增加應對場景變化的能力。
在本篇論文中,我們有以下幾個主要貢獻:自動車道方向偵測、較靈活的特徵之擷取使用、具有穩健預測表現的自適應性分群法以及穩定系統預測表現的雜訊濾除步驟。其中在自動車道方向偵測的部分,我們利用特徵點匹配以及方向量化的方式來估計主要的車流方向,用以達到自動偵測車道方向的結果。而在特徵擷取的部分,我們使用的是前景之邊緣資訊所投影統計出之直方圖。為了搭配這樣的特徵並且使系統更加靈活,我們採用了自適應性高的分群演算法來預測出每張影像中所含有的車輛數,而不考慮需要訓練模組的方法,此演算法自適性高的原因來自於其中的參數設定,我們參數的決定方法是根據車道線數、感興趣區域以及車道角度的資訊來動態決定的。而使用前述的幾個重要貢獻,可以使我們對於多變的監控影像有更好的應對能力,這正是我們系統的主要目標,也是我們選擇不使用需要提前訓練模組的方法的原因。
而在雜訊濾除的部分,我們去除了一些不連續的值,經過這個步驟可以使我們系統的表現更加穩定。最後,我們使用一個機率式的有限狀態轉換機的圖形比對方法,他可以將連續單張影像中預測出之車輛數混合比對計算出此段時間之總車輛數。此方法之比對模型是利用車輛序列之長度以及車輛序列的轉換狀態相似程度,兩種特徵訓練而成。在此篇論文中是利用平均絕對誤差來評斷車流量估測的錯誤程度,越低代表具有越好的預測表現。而我們利用這樣的評斷方式可以看出,我們所提出之系統可以有效率的分析受雨滴汙染之影片中所含有的車流量。

In this study, we propose a flexible traffic flow estimation system for intelligent highway surveillance applications to deal with rain-drop tampered conditions. Major contributions of the proposed approach are automatic lane direction detection, flexible feature extraction, robust estimation with adaptive clustering, and noise removal for steady estimation.

For automatic lane direction detection, we use feature point matching and direction quantization to estimate the main direction. For feature extraction, we use projected foreground moving edge information. When estimating vehicle count for each frame, we utilize adaptive clustering algorithm instead of modeling methods. The parameters for the clustering algorithm are adaptively determined according to the region of interest and the number of lanes. The advantages of using clustering instead of model training is to minimize the need for retraining estimation models for different surveillance scenes.

For noise removal, we eliminate some discontinuous values. This step can make our system more stable. Finally, a flexible graph-based mapping method is incorporated to map the per frame vehicle count to per minute traffic flow. The mapping models are trained based on the length of the vehicle count sequence and the state transition likelihoods. The accuracy of the traffic flow analysis is satisfying with a low mean absolute error. It demonstrates that the proposed system is effective to analyze traffic flow under rain-drop tampered highway surveillance cameras.

目錄
摘要 V
ABSTRACT VI
致謝 VII
目錄 VIII
圖目錄 X
表目錄 XIII
第一章 緒論 1
1.1 研究動機 1
1.2 相關研究 5
1.3 系統流程 7
1.4 論文架構 10
第二章 雨滴破壞下監控影片之車流量估計 11
2.1 雨滴影像中之車輛數估計 12
2.1.1 選取車流的偵測區域 12
2.1.2 雨滴破壞影像之前景區域偵測 13
2.1.2.1 顯著區域偵測[Salient Region Detection] 13
2.1.2.2 高斯混合模型之前背景判斷 [Background Subtraction using Gaussian Mixture Model] 14
2.1.2.3 連續影像相減法[Temporal Differencing] 14
2.1.3 前景區域之特徵擷取 - 影像投影統計直方圖 14
2.1.4.1 估計雨滴破壞影像之車輛數 - 分群演算法 19
2.2 受雨滴汙染影片之車流量估計 21
2.2.1 雜訊濾除[Noise Removal] 22
2.2.2 車輛數序列對應車輛數模型 [Vehicle Number Sequence Mapping Model] 23
第三章 實驗結果與比較 28
3.1 實驗環境與實驗資料 28
3.2 前景抓取成果之比較 30
3.3 影像中車輛數之估計成果比較與分析 31
3.4 車流量分析之成果及比較 40
3.4.1 序列模型法於自身場景之車流量分析 41
3.4.2 序列模型法於單一場景訓練下 測試綜合場景之成果比較 51
第四章 結論與未來研究方向 56
參考文獻 58



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