本文選題：BP神經網絡 + 區(qū)域分類器��； 參考：《大連海事大學》2017年碩士論文

【摘要】：二十世紀以來,汽車的擁有量顯著增長,隨之而來的交通事故也發(fā)生的越來越頻繁,人們對交通安全問題的關注與日俱增,世界各國的交通部正積極采取一些有效措施,減少交通事故的發(fā)生。因此,基于駕駛員的視線追蹤系統(tǒng)便應運而生。但現(xiàn)有的系統(tǒng)一般局限于簡單場景,且必須在前期做好校準工作的情況下進行的,而對于不加約束的人、無校準、光照變化等問題仍存在很大的研究空間,其實時性、精確性和魯棒性與實際應用之間還存在很大距離。針對這些問題,本文提出基于BP神經網絡的無校準駕駛員注視區(qū)域估計方法,將重點從頭部姿態(tài)和瞳孔視線角度參數(shù)獲取、BP神經網絡注視區(qū)域估計算法,及實驗對比評估三個部分進行研究,主要工作如下:首先,本文需要先獲取頭部姿態(tài)和視線方向的角度參數(shù),在此過程中,針對駕駛員身體出現(xiàn)左右晃動,或者不同駕駛員身高不同,而發(fā)生相對于攝像機的左右偏移和上下偏移問題,提出一種基于幾何關系的頭部姿態(tài)校正算法。同時,本文通過建立3D眼球模型進行瞳孔視線方向估計。然后,構建了一個基于BP神經網絡的無校準駕駛員注視區(qū)域估計系統(tǒng)。通過BP神經網絡模型對駕駛員在駕駛過程中的頭部姿態(tài)及視線角度參數(shù)進行訓練并構建區(qū)域分類器,并通過該網絡模型進行駕駛員無校準注視區(qū)域估計。最后,對本文方法進行評估。通過對比實驗表明,本文提出的方法不僅能滿足學術研究的要求,而且能實現(xiàn)在復雜環(huán)境下駕駛員的注視區(qū)域估計,滿足了對實驗的實時性,精確度和魯棒性的要求,并為安全駕駛的輔助系統(tǒng)打下良好的基礎。
[Abstract]:Since twentieth Century, the number of cars has increased significantly, and the traffic accidents are becoming more and more frequent. People pay more attention to traffic safety. The transportation department of the world is actively taking some effective measures to reduce the occurrence of traffic accidents. However, the existing systems are generally limited to simple scenes, and must be carried out in the early stage of calibration, but there is still a lot of research space for unconstrained people, no calibration, light change and other problems, and there is a great distance between the reality, the accuracy and the robustness. An uncalibrated area estimation method based on BP neural network, which focuses on the head attitude and the eye view angle parameters, the BP neural network fixation area estimation algorithm, and the experimental comparison and evaluation of three parts are studied. The main work is as follows: first, we need to obtain the angle reference of the head attitude and the direction of sight. In this process, a head attitude correction algorithm based on the geometric relationship is proposed for the driver body sloshing, or different driver's height, and the left and right offset and up and down migration of the camera. At the same time, the 3D eye model is established to estimate the eye direction of the pupil. Then, the construction of the eye direction is constructed. An uncalibrated area estimation system based on BP neural network is introduced. The BP neural network model is used to train the driver's head attitude and view angle parameters in the driving process and construct a regional classifier, and the driver's uncalibrated gaze area estimation is carried out through the network model. Finally, the method is carried out. A comparative experiment shows that the proposed method can not only meet the requirements of academic research, but also realize the estimation of the driver's gaze area under the complex environment, meet the requirements of the real-time, accuracy and robustness of the experiment, and lay a good foundation for the auxiliary system of safe driving.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP183

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本文編號：2091573