臺灣博碩士論文加值系統

道路資訊的蒐集，在許多的交通運輸應用中扮演著重要的角色；目前政府交通單位在各種道路上所裝設之攝影機，其所拍攝到的影像在傳回交控中心後，均由人工監看；由於一個人通常需兼顧多部攝影機，難免造成疏失，而且也無法即時計算交通數據。本論文針對交通監測影像，提出能自動從影像中擷取交通資訊的技術，主要目的在於利用現有的交通攝影設備，蒐集道路上之車流量及車行速度等資訊，以應用於交通監控系統。在擷取交通資訊的過程中，主要可以分成三步驟：車輛偵測、車輛追蹤及交通資訊擷取。首先從輸入的影像序列中，以一種結合建構及更新的方法產生背景影像，之後將建構出來的背景影像與目前影像作比較後，快速偵測出前景區塊；另外還利用光線估測的方法評估影像中是否含有陰影，若其含有陰影，則執行陰影移除的工作以擷取出車輛影像。在偵測出車輛後，便利用車輛本身及其互相之間的屬性作比對，以追蹤連續影像中的車行路徑及車輛數。接著利用攝影機的校對的結果，將車行資訊轉換成真正的空間位移，以計算出車行速度。最後由車輛數及車行速度推算出各項交通參數值，以擷取所需之交通資訊。我們以家用攝影機及交通單位所提供的監測影像驗証所提出的方法，證明其不但能適用於各種交通狀況，還能相當正確的擷取出交通資訊。

In this paper, a vision-based traffic measurement system is addressed. The objective of this system is twofold, automatically count the vehicles passing through a roadway and measure their speeds. The collected data will be used to compute a number of traffic parameters, including flow rate, demand, time headway, throughput, mean speed, and density. This system consists of one off-line module (preprocessing) and three on-line modules (vehicle detection, vehicle tracking, and calculation of traffic parameters). In the preprocessing component, four off-line tasks are performed, background image generation, lane detection, vanishing point detection, and camera calibration. The background image is used by the vehicle detection module to quickly extract foreground objects from video images through image subtraction. Foreground objects are tracked across the video sequence by the tracking module. Once the traces of vehicles are determined, the trace count gives the number of vehicles. The 2D traces are converted into 3D ones using the transformation functions determined during camera calibration. The speeds of vehicles are then calculated. Based on the number of vehicles and their speeds, the desired traffic parameters are computed. We present the results of some experiments using real image sequences made using a home video camera and a traffic surveillance system. Finally our conclusions show that our proposed system is both adaptable and accurate.

圖表目錄.... ii
第一章 簡介.................. 1-1
1.1 交通監測系統. 1-1
1.2 文獻探討......... 1-2
1.3 論文架構......... 1-6
第二章 系統架構......... 2-1
2.1 系統設置......... 2-1
2.2 系統運作......... 2-1
第三章 前處理............. 3-1
3.1 建構背景影像. 3-1
3.2 道路消逝點..... 3-5
3.3 攝影機校對... 3-10
3.4 位移修正....... 3-12
第四章 車輛偵測......... 4-1
4.1 前景影像的計算............. 4-1
4.2 明亮度評估..... 4-1
4.3 陰影偵測及移除............. 4-3
第五章 車輛追蹤......... 5-1
5.1 車輛屬性及路徑參數......... 5-1
5.2車行路徑之決定............. 5-2
5.3 Occlusion及Separation的問題............. 5-3
第六章 交通參數......... 6-1
6.1 車輛數目與車行速度......... 6-1
6.2 交通參數......... 6-1
第七章 實驗結果......... 7-1
7.1 一般攝影機拍攝影像......... 7-1
7.2 高速公路局監控影像......... 7-4
第八章 結論及未來方向.................. 8-1
參考文獻. 8-3
圖表目錄
第二章 系統架構..............
圖2.1 系統設置狀況............. 2-4
圖2.2 道路監測影像............. 2-4
圖2.3 各類道路攝影監測設備. 2-5
圖2.4 高速公路交通控制中心及其監控影像............. 2-5
圖2.5 視覺式交通監測系統架構圖............. 2-6
圖2.6背景影像..... 2-6
圖2.7偵測出來的車道標線..... 2-6
圖2.8移除陰影後的車輛影像. 2-7
圖2.9追蹤到的車行路徑......... 2-7
第三章 前處理...................
圖3.1 漸進式建構背景影像之流程圖....... 3-14
圖3.2 差異影像... 3-14
圖3.3 每一像點所對應的histogram........ 3-14
圖3.4 灰階值的程度值變化... 3-15
圖3.5 confidence table 3-16
圖3.6 初期之背景影像........... 3-16
圖3.7 各種背景影像建構法之比較........... 3-17
圖3.8 偵測道路標線........... 3-18
圖3.9 steerable filters的使用 3-18
圖3.10 影像中某像點在各方向上的強度值........... 3-18
圖3.11 G2及H2的basis filters........... 3-19
圖3.12 路面消逝點........... 3-19
圖3.13 影像平面與實際空間的關係....... 3-20
圖3.14 影像中車輛的中心位置及其實際的位置....... 3-21
圖3.15 物體在影像平面及實際空間的位移情況....... 3-21
表3.1 G2及H2之basis filters函數及其interpolation functions...... 3-22
表3.2 E2(θ)之fourier series展開式.... 3.23
第四章 車輛偵測..............
圖4.1 前景物擷取. 4-9
圖4.2 物體擋住光線所形成的兩種陰影..... 4-9
圖4.3 不同時段及不同天氣下所計算出來的E值及P值........... 4-10
圖4.4 陰影移除之流程圖........ 4-11
圖4.5 依車輛區塊所計算出的明亮度值.... 4-11
圖4.6區塊內部靠近邊緣的像點........... 4-12
圖4.7決定陰影方向的四個參數........... 4-12
圖4.8 樣本像點的擷取........... 4-12
圖4.9前景區塊及背景區塊之邊線........... 4-13
圖4.10車輛邊線... 4-13
圖4.11 分離被陰影連結的車輛........... 4-13
圖4.12 利用邊線重建車輛影像........... 4-14
圖4.13 各種陰影移除的例子 4-14
第五章 車輛追蹤..............
圖5.1 車輛追蹤時所使用的各項參數......... 5-6
圖5.2 車輛追蹤時所使用的參數關係圖..... 5-7
圖5.3 連續影像中車輛之配對情況............. 5-8
圖5.4 由連續三張影像所比對出來的車行路徑............. 5-9
圖5.5 連續追蹤後的車行路徑. 5-9
圖5.6 車行路徑occlusion的情況........... 5-10
圖5.7 車輛separation的追蹤結果... 5-10
圖5.8 車輛passing的追蹤結果............ 5-11
第六章 交通參數..............
圖6.1 利用車行進路徑計算車輛數............. 6-4
圖6.2 以通過某標定線為準計算車輛數..... 6-4
第七章 實驗結果..............
圖7.1 影像中之車輛由左上往右下行進之實驗結果..... 7-6
圖7.2 正常之追蹤結果............. 7-6
圖7.3 車輛發生occlusion之追蹤情況......... 7-6
圖7.4 影像中之車輛由左下往右上行進之實驗結果..... 7-6
圖7.5 車輛影像重疊造成誤判的情況......... 7-7
圖7.6 同時監測不同行進方向的車輛......... 7-7
圖7.7 經由mask擷取出的偵測範圍......... 7-7
圖7.8 車輛較不易重疊的拍攝角度............. 7-8
圖7.9 高速公路局的監測影像. 7-8
圖7.10 影像不穩定所造成的誤判............. 7-9
圖7.11 高速公路局雨天時的監測影像..... 7-9
圖7.12 因車燈在路面的反射所造成的誤判............. 7-9
圖7.13 高速公路局傍晚的監測影像....... 7-10
圖7.14 有些車輛難以辨識所造成的誤判 7-10

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