【摘要】：隨著計算機技術(shù)的迅速發(fā)展,智能視頻監(jiān)控技術(shù)在人們的日常生活中有著非常廣泛地應(yīng)用,同時給人們的日常生活帶來了極大的便利,運動目標(biāo)的檢測與跟蹤是智能視頻監(jiān)控技術(shù)中的關(guān)鍵性問題。運動目標(biāo)的檢測與跟蹤是計算機視覺領(lǐng)域一個非常熱門的課題,在人工智能、模式識別、圖像處理、醫(yī)學(xué)成像等多個領(lǐng)域有著非常廣泛地應(yīng)用,更是很多視覺應(yīng)用系統(tǒng)的重要組成部分,如智能視頻監(jiān)控、人機交互、智能駕駛等。復(fù)雜場景下目標(biāo)本身地變化以及背景環(huán)境地變化也是該技術(shù)面臨的一項挑戰(zhàn)。因此,對于復(fù)雜場景下運動目標(biāo)的檢測與跟蹤的研究有著非常重要的研究價值和現(xiàn)實意義。本文的主要工作如下:(1)針對混合高斯模型在背景建模時采用固定的更新率,在復(fù)雜場景下對運動目標(biāo)不能進行準(zhǔn)確地檢測,提出了一種改進的混合高斯算法,該算法在混合高斯模型的基礎(chǔ)上結(jié)合改進的幀間差分法,將背景劃分為不同區(qū)域,同時對不同區(qū)域選取適合的更新率,使得背景模型能夠更好的適應(yīng)復(fù)雜場景中的外界干擾。實驗結(jié)果表明,本文提出的改進的混合高斯算法能夠適應(yīng)復(fù)雜場景下光照變化、樹木遮擋等外界環(huán)境的干擾,提高了檢測的準(zhǔn)確性。(2)針對粒子濾波在重采樣過程中舍棄低權(quán)值的粒子,所造成的粒子退化問題,對粒子濾波進行了改進,提出了一種基于螢火蟲算法的粒子濾波算法。該算法在重要性采樣過程中,使用螢火蟲算法對粒子進行迭代尋優(yōu),使得粒子更接近后驗概率分布,在重采樣過程中利用螢火蟲算法思想,使粒子模擬螢火蟲向高似然區(qū)域移動,并更新全局最優(yōu)值,提高粒子的有效性及多樣性。實驗結(jié)果表明,在背景發(fā)生變化、不規(guī)則的運動、以及樹木的遮擋等多因素的影響下,本文提出的算法能夠使用更少的粒子數(shù)目,進行高效、精確的目標(biāo)跟蹤。(3)本文在VS2010環(huán)境下,采用MFC界面類庫和計算機視覺類庫OpenCV,設(shè)計并實現(xiàn)了具有視頻輸入、圖像預(yù)處理、運動目標(biāo)檢測、形態(tài)學(xué)處理、目標(biāo)跟蹤以及目標(biāo)軌跡繪制等功能的視頻監(jiān)控系統(tǒng)。其中目標(biāo)檢測以及目標(biāo)跟蹤模塊分別使用本文所提出的檢測和跟蹤方法。
[Abstract]:With the rapid development of computer technology, intelligent video surveillance technology has been widely used in people's daily life, at the same time, it has brought great convenience to people's daily life. Detection and tracking of moving targets is a key problem in intelligent video surveillance technology. Moving target detection and tracking is a very hot topic in the field of computer vision. It is widely used in artificial intelligence, pattern recognition, image processing, medical imaging and other fields. It is also an important component of many visual application systems, such as intelligent video surveillance, human-computer interaction, intelligent driving and so on. It is also a challenge to change the target itself and the background environment in complex scenarios. Therefore, the research of moving target detection and tracking in complex scene has very important research value and practical significance. The main work of this paper is as follows: (1) aiming at the fixed updating rate of the mixed Gao Si model in the background modeling, the moving target can not be detected accurately in the complex scene, so an improved mixed Gao Si algorithm is proposed. Based on the mixed Gao Si model and the improved inter-frame difference method, the background is divided into different regions, and the suitable updating rate is selected for the different regions, so that the background model can better adapt to the external interference in the complex scene. The experimental results show that the improved mixed Gao Si algorithm proposed in this paper can adapt to the external environment interference, such as illumination variation, tree occlusion and so on. The accuracy of detection is improved. (2) aiming at the problem of particle degradation caused by particle filter abandoning low weight particles in the process of resampling, the particle filter is improved, and a particle filter algorithm based on firefly algorithm is proposed. In the process of importance sampling, the algorithm uses the firefly algorithm to iteratively optimize the particle, so that the particle is closer to the posteriori probability distribution, and the idea of the firefly algorithm is used in the process of resampling. The particle simulates the firefly to move to the high likelihood region, and updates the global optimum value, enhances the particle validity and the diversity. The experimental results show that under the influence of many factors, such as background change, irregular motion and tree occlusion, the proposed algorithm can use fewer particles and be efficient. (3) in the environment of VS2010, this paper designs and implements video input, image preprocessing, moving target detection, morphological processing by using MFC interface class library and computer vision class library OpenCV,. Video surveillance system for target tracking and target trajectory rendering. Target detection and target tracking module respectively use the detection and tracking methods proposed in this paper.
【學(xué)位授予單位】：江蘇大學(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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