【摘要】：近年來隨著科學(xué)水平的發(fā)展與計算機(jī)技術(shù)的進(jìn)步,運(yùn)動目標(biāo)檢測與跟蹤技術(shù)取得了許多成果,期間涌現(xiàn)出不少優(yōu)秀算法,實際應(yīng)用領(lǐng)域也愈加寬廣。然而,在復(fù)雜環(huán)境下,依舊存在著很多問題。針對運(yùn)動目標(biāo)檢測與跟蹤算法存在的不足,本文提出了一種新的運(yùn)動目標(biāo)檢測與跟蹤算法�？偟膩碚f,本文主要研究工作和創(chuàng)新性如下:1、圖像處理基礎(chǔ)知識的介紹。本文概括介紹了圖像平滑處理、邊緣檢測和數(shù)學(xué)形態(tài)學(xué)處理的相關(guān)知識,針對本文所涉及的一些處理技術(shù),包括中值濾波、Canny算子、膨脹腐蝕等進(jìn)行了詳細(xì)介紹。2、運(yùn)動目標(biāo)檢測算法的研究。首先,介紹了傳統(tǒng)的光流法、幀間差分法及背景差分法的基礎(chǔ)概念及公式原理,并對其中幾種代表性的算法進(jìn)行分析對比,列出了它們的優(yōu)缺點(diǎn)。然后,在此基礎(chǔ)上提出了一種基于視覺背景提取模型的新算法——IDVibe算法。該算法分別從模型建立、模型匹配、模型更新及前景分割四個方面進(jìn)行改進(jìn),通過融入三幀差分法的思想,有效地解決了“鬼影”、光照變化等問題。最后,通過實驗仿真可以得到,本文提出的檢測算法能更好地適應(yīng)動態(tài)復(fù)雜的環(huán)境,有良好的檢測效果和魯棒性。3、運(yùn)動目標(biāo)跟蹤算法的研究。本文以卡爾曼濾波跟蹤算法、Mean shift算法和粒子濾波算法為基礎(chǔ),提出了一種融合多特征與Mean Shift的粒子濾波跟蹤算法。首先,通過IDVibe算法對運(yùn)動目標(biāo)進(jìn)行檢測、定位。然后,融合顏色、紋理及邊緣的特征信息進(jìn)行模型匹配,實現(xiàn)粒子濾波跟蹤。最后,運(yùn)用Mean shift算法的收斂性,將粒子重新聚集到真實目標(biāo)附近,實現(xiàn)運(yùn)動目標(biāo)的跟蹤。通過實驗結(jié)果可以證明,本文提出的跟蹤算法能達(dá)到較好的跟蹤效果,對比以往單一的跟蹤算法有更高的準(zhǔn)確性與實時性。
[Abstract]:In recent years, with the development of science and computer technology, many achievements have been made in moving target detection and tracking technology. During this period, many excellent algorithms have emerged, and the practical application field has become wider and wider. However, in the complex environment, there are still many problems. A new algorithm for moving target detection and tracking is proposed in this paper. In general, the main work and innovation of this paper are as follows: introduction of basic knowledge of image processing. This paper summarizes the knowledge of image smoothing, edge detection and mathematical morphology processing. Some processing techniques, including median filter and Canny operator, are discussed in this paper. The paper introduces in detail the. 2. 2, the research of moving target detection algorithm. Firstly, the basic concepts and formula principles of traditional optical flow method, inter-frame difference method and background difference method are introduced, and several representative algorithms are analyzed and compared, and their advantages and disadvantages are listed. Then, a new algorithm based on visual background extraction model, IDVibe algorithm, is proposed. The algorithm is improved from four aspects: model establishment, model matching, model updating and foreground segmentation. By incorporating the idea of three-frame difference method, the problems of "ghost" and illumination change are effectively solved. Finally, through the experiment simulation, we can get that the detection algorithm proposed in this paper can better adapt to the dynamic and complex environment, have good detection effect and robustness. 3, the research of moving target tracking algorithm. Based on the Kalman filter tracking algorithm, mean shift algorithm and particle filter algorithm, a particle filter tracking algorithm combining multiple features and Mean Shift is proposed in this paper. First, the moving targets are detected and located by IDVibe algorithm. Then, the feature information of color, texture and edge are fused to match the model to realize particle filter tracking. Finally, using the convergence of the Mean shift algorithm, the particles are reassembled near the real target to achieve the tracking of moving targets. The experimental results show that the proposed tracking algorithm can achieve better tracking effect and has higher accuracy and real-time performance than the previous single tracking algorithm.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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