【摘要】：視頻車輛檢測是一種在視頻序列中提取運動車輛對象的技術(shù),其廣泛運用于視頻監(jiān)控、智能交通檢測等系統(tǒng)中。由于運動車輛檢測技術(shù),特別是相機運動條件下的視頻車輛檢測,具有復雜性、多變性等特點,該技術(shù)仍處于起步階段,需要不斷的研究并加以改進。為了在相機運動條件下準確檢測出車輛,本文圍繞著以下幾個方面來開展研究工作:(1)對本文涉及到的理論基礎(chǔ)知識進行學習研究�？偨Y(jié)概括涉及視頻圖像處理,尤其是視頻目標檢測等相關(guān)國內(nèi)外文獻資料;篩選并學習基于相機運動條件下視頻車輛檢測的相關(guān)知識,重點是近幾年關(guān)于動攝像頭下視頻車輛檢測研究成果;針對動攝像頭視頻車輛檢測研究現(xiàn)狀,并結(jié)合自身知識積累,構(gòu)建本文算法框架。(2)對視頻車輛檢測方法進行分析研究。視頻車輛檢測方法可根據(jù)攝像頭是否運動進行分類,本論文簡單介紹了靜攝像頭下視頻運動車輛檢測方法,具體介紹相機運動條件下的視頻車輛檢測方法。(3)對相機運動條件下的視頻車輛檢測進行算法設(shè)計。算法分為四部分。第一部分全局運動估計與補償算法,并對基礎(chǔ)算法進行說明,在這基礎(chǔ)上,通過分析不同的全局運動估計方法的優(yōu)缺點,提出使用六參數(shù)仿射變換模型,估計仿射變換參數(shù),然后對仿射變換后的圖像進行補償。第二部分高斯差分算法,改進了運動補償后的差分步驟,以便獲得更好的檢測效果。第三部分非參數(shù)核密度估計算法,以此對檢測進行優(yōu)化。第四部分使用矩形框?qū)z測出的車輛進行目標定位。(4)對本論文的算法進行實驗驗證。利用VS2010和Matlab軟件平臺,并結(jié)合OpenCV開源庫,編寫本算法的實驗仿真程序,以動攝像頭拍攝的視頻為輸入,進行實驗。實驗驗證過程分為兩部分:第一部分實驗用來驗證本文算法的功能實現(xiàn);第二部分實驗是對比試驗,用來驗證本文算法的準確性和高魯棒性。研究創(chuàng)新有兩點:(1)為了減小攝像頭運動對視頻中運動目標檢測的影響,提高運動估計的準確度,在進行仿射運動估計時,對目標幀的前后幀采用不同的仿射變換矩陣,計算量降低了,仿射變換效果提高了。(2)使用非參數(shù)核密度估計對得到的檢測目標進行優(yōu)化,減少各環(huán)節(jié)帶來的目標空洞問題和噪聲靈敏度問題的影響。研究的不足是,仿射運動估計參數(shù)的獲取,計算量較大,時間較長,仿射運動估計參數(shù)的獲取速度仍有待提高,算法的天氣情況適應性,需要進一步研究及改進。
[Abstract]:Video vehicle detection is a technique for extracting moving vehicle objects in video sequence. It is widely used in video surveillance and intelligent traffic detection systems. Because of the characteristics of vehicle detection technology, especially video vehicle detection under camera motion conditions, it is still in its infancy. In order to detect the vehicle accurately under the camera movement, this paper focuses on the following aspects: (1) study and study the theoretical basic knowledge involved in this article. Summarize and summarize the related domestic and foreign documents related to video image processing, especially the visual frequency target detection. And learn the related knowledge of video vehicle detection based on camera motion. The focus is on the research results of video vehicle detection in recent years. In view of the current situation of video vehicle detection research in mobile camera, and combining with the knowledge accumulation, this paper constructs the algorithm framework. (2) video vehicle detection methods are analyzed and studied. The vehicle detection method can be classified according to the motion of the camera. This paper briefly introduces the video moving vehicle detection method under the static camera and introduces the video vehicle detection method under the camera motion condition. (3) the algorithm is designed for the video vehicle detection under the camera motion condition. The algorithm is divided into four parts. The first part is global. The motion estimation and compensation algorithm and the basic algorithm are explained. On this basis, by analyzing the advantages and disadvantages of different global motion estimation methods, the six parameter affine transform model is used to estimate the affine transformation parameters, and then the affine transform image is compensated. The second part of the Gauss difference algorithm is improved after the motion compensation. The third part of the non parametric kernel density estimation algorithm is used to optimize the detection. The fourth part uses the rectangle frame to target the detection of the vehicle. (4) the experimental verification of the algorithm in this paper. Using the VS2010 and Matlab software platform, and combining the OpenCV open source library, write this The experimental simulation program of the algorithm is carried out with the video taken by the moving camera as input, and the experiment is divided into two parts: the first part is used to verify the function realization of the algorithm. The second part of the experiment is a contrast test, which is used to verify the accuracy and robustness of the algorithm. (1) to reduce the perturbation. Like the effect of head motion on moving target detection in video, the accuracy of motion estimation is improved. When carrying out the affine motion estimation, different affine transform matrices are used for the front and back frames of the target frame. The amount of computation is reduced and the effect of affine transformation is improved. (2) the detection targets are optimized by using non parametric kernel density estimation, and each of them is reduced. The problem of the target cavitation and the noise sensitivity caused by the link. The shortage of the research is that the parameters of the affine motion estimation are obtained, the computation is large and the time is long. The acquisition speed of the affine motion estimation parameters remains to be improved, and the weather conditions of the algorithm are adaptable, and further research and improvement are needed.
【學位授予單位】：山東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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