本文選題：視頻車輛輪廓 + adaboost算法��； 參考：《中原工學院》2017年碩士論文

【摘要】：視頻車輛輪廓檢測技術作為智能交通中的關鍵技術,在日常生活中有著廣泛的應用前景。而智能交通對于視頻車輛輪廓檢測技術也有著實時性和準確性等嚴苛的要求。而在檢測時,視頻圖像中復雜的背景以及各式各樣的干擾,是目前視頻車輛輪廓檢測技術面臨的問題。而隨著國內外學者的不斷努力,各式各樣的檢測算法層出不窮。Adaboost算法是近些年來比較流行的機器學習算法,在人臉識別領域有著出色的表現(xiàn)。同樣,adaboost算法可以應用于視頻車輛輪廓檢測領域。本文根據(jù)經典的adaboost算法,在其基礎上做出了一些改進,研究工作如下:(1)介紹視頻車輛輪廓檢測技術研究的背景意義,了解其中面臨的問題。(2)介紹分析幾種現(xiàn)有的視頻車輛輪廓檢測算法,了解其原理,并總結出存在的問題。(3)詳細了解adaboost算法,理解其原理和實現(xiàn)過程,依次介紹haar特征、積分圖以及分類器的訓練及選取過程。(4)提出一種改進的adaboost算法進行視頻車輛輪廓檢測。首先,針對算法學習過程中,haar特征計算量過于龐大且耗時的現(xiàn)象,提出了對訓練樣本進行裁剪,去除樣本邊緣像素,有效減少特征數(shù)量,從而降低了計算量。(5)Adaboost算法檢測在對視頻圖像進行檢測時,滑動子窗口會在待檢圖像上依次滑過,圖像中無關信息都需要被檢測一遍,相當耗時。提出了使用光流法來獲取視頻圖像中的運動區(qū)域作為感興趣區(qū)域,在感興趣區(qū)域中使用canny算子進行邊緣檢測,通過邊緣能量篩選感興趣區(qū)域,排除非感興趣區(qū)域。最終使用adaboost算法對感興趣區(qū)域進行檢測,減小了檢測區(qū)域降低了檢測時間。(6)通過設置閾值來對檢測結果進行篩選,提升準確率。結合算法的優(yōu)點和不足對未來的發(fā)展進行展望。
[Abstract]:As the key technology of intelligent transportation, video vehicle contour detection technology has a wide application prospect in daily life. Intelligent Transportation has strict requirements such as real-time and accuracy for video vehicle contour detection technology. In detection, the complex background and various kinds of interference in video images are the current problems of video vehicle contour detection technology. With the continuous efforts of scholars at home and abroad, a variety of detection algorithm. Adaboost algorithm is a popular machine learning algorithm in recent years, and has a good performance in the field of face recognition. The adaboost algorithm can also be used in the field of video vehicle contour detection. Based on the classical adaboost algorithm, some improvements are made in this paper. The research work is as follows: 1) the background significance of the video vehicle contour detection technology is introduced. (2) introduce and analyze several existing video vehicle contour detection algorithms, understand their principles, and summarize the existing problems. (3) understand the adaboost algorithm in detail, understand its principle and implementation process, and then introduce the characteristics of haar in turn. An improved adaboost algorithm is proposed for video vehicle contour detection. First of all, aiming at the phenomenon that the computation of haar feature is too large and time-consuming in the learning process of the algorithm, the training sample is clipped to remove the edge pixels of the sample, and the number of features is reduced effectively. Therefore, the computational complexity of the algorithm is reduced. When detecting the video image, the sliding subwindow will slip through the image to be checked, and the irrelevant information in the image needs to be detected once, which is time-consuming. In this paper, an optical flow method is proposed to obtain the moving region of the video image as the region of interest, and the canny operator is used to detect the edge of the region of interest. The region of interest is filtered by edge energy, and the region of non-interest is excluded. Finally, the adaboost algorithm is used to detect the region of interest, which reduces the detection time and reduces the detection time. Combining the advantages and disadvantages of the algorithm, the future development is prospected.
【學位授予單位】：中原工學院
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495;TP391.41

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