臺灣博碩士論文加值系統

車輛駕駛人常因為車體本身的視覺盲點而造成交通意外，現有的車輛輔助系統常利用警示燈號或聲響來提供駕駛車輛環周狀況。由於鏡頭的售價漸趨便宜，目前車廠也將攝影機架設於車輛周圍，並將攝取到的影像或是車輛上方的鳥瞰影像顯示於車內，作為安全駕駛輔助視訊系統。相對於固定的鳥瞰視角，我們提出了一使用第三人稱視角的車輛環周監視系統─“天使之眼”。我們接合車輛四周的魚眼攝影機所擷取的影像，將環周影像投影在一混合式3D 模型中，並提供不同視角的影像，使得駕駛者可自由轉換視角。
但因系統未知車輛周圍前景物的深度，在視覺呈現上，會有前景物的影像扭曲或是鬼影的現象產生。所以我們將深度攝影機加裝於汽車上，並透過擷取到的障礙物深度資訊，設計了一個會隨深度資訊不同而調整的3D 投影模型，以解決影像接合處的鬼影問題，並減低車輛周圍物體的影像扭曲。

Driving assistance systems help drivers to avoid car accidents by provid-ing warning signals or visual cues of surrounding situations. Instead of the fixed bird’s-eye view monitoring proposed in many previous works, we de-veloped a real-time vehicle surrounding monitoring system, ”Angel Eye”, that can assist drivers to perceive the vehicle surrounding situations more easily. In our system, four fisheye cameras are mounted around a vehicle. To inte-grate these four fisheye camera views, we firstly use fisheye camera calibra-tion method to dewarp the captured images into perspective projection ones. Then, we calculated the camera intrinsic parameters and homography trans-form matrix to get the camera extrinsic parameters. To stitch these dewarpped images, we projected undistorted images into a 3D hybrid projection model and finally the images of the selected viewpoint are rendered.
However, the unknown position of foreground obstacles would cause some visual noises, like image distortion of objects or ghost effect. So we add depth camera into previous system to obtain the depth information of foreground obstacles. The proposed 3D model can be adjusted based on the distance between vehicle and foreground obstacles. The depth-adaptive model can fa-cilitate the rendering of vehicle surroundings in a more realistic and correct way.

口試委員審定書 i
致謝 ii
中文摘要 iii
Abstract iv
Contents v
List of Figures vii
1 Introduction 1
1.1 Background and Motivation . . . 1
1.2 Organization of the Thesis . . . 3
2 Related Work 4
2.1 Vehicle Surrounding Monitoring System . . . 4
2.2 Model Construction in 3D Scene . . . 5
3 Preprocessing 6
3.1 Fisheye Camera Calibration and Intrinsic Parameters . . . 6
3.2 Homography and Extrinsic Parameters . . . 8
4 Vehicle Surrounding Monitoring Using Static 3D Model 10
4.1 View Interpolation Using Static 3D Model . . . 11
4.2 Model Comparison . . . 12
4.3 Lookup Table . . . 14
4.4 Experimental Setup . . . 14
4.5 Results and Discussion . . . 15
5 Vehicle Surrounding Monitoring Using Depth-Adaptive 3D Model 17
5.1 Depth Image Processing . . . 17
5.2 Depth-Adaptive Model Construction . . . 21
5.3 Rendering Results and Discussion . . . 22
6 Conclusion and Future Work 25
References 26

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