【摘要】：首先本文具體介紹了運動目標識別與跟蹤方法在國內(nèi)外的發(fā)展與研究的狀況,并且對相關(guān)的算法做了簡要的說明。然后針對目標識別與跟蹤方法,本文分別詳細介紹和說明了粒子濾波跟蹤算法與Camshift跟蹤算法,并對兩種跟蹤算法分別做了相應(yīng)的對比跟蹤實驗來說明各自的特性及其優(yōu)缺點。對兩種跟蹤算法所適用的跟蹤環(huán)境進行了分析,當(dāng)目標與背景顏色差異比較大時,粒子濾波跟蹤算法和Camshift算法能夠有效的跟蹤目標,但是當(dāng)目標與背景顏色差異較小或者目標處于復(fù)雜背景區(qū)域時目標跟蹤就會產(chǎn)生偏差,甚至無法準確的跟蹤目標。為提高復(fù)雜背景下上述兩種跟蹤算法的穩(wěn)定性與準確性,在兩種基本算法的基礎(chǔ)上,分別提出了各自的改進算法來改善其在復(fù)雜背景下的跟蹤性能。提出基于顯著性直方圖模型的粒子濾波跟蹤方法。通過對比目標與背景區(qū)中像素色調(diào)的分布,確定出不同色調(diào)等級的顯著性權(quán)值,從而建立起目標的顯著性直方圖模型。顯著性直方圖模型可抑制背景中與目標具有相似色調(diào)的區(qū)域?qū)δ繕俗R別的干擾,突出目標顯著色調(diào)在目標識別中的作用,從而提高目標識別的準確性。提出了基于邊緣抑制的Camshift跟蹤算法。利用上一幀目標的位置和大小通過權(quán)值函數(shù),在反向投影圖中降低目標邊緣的亮度權(quán)值,被抑制的邊緣可以有效的區(qū)分目標和背景,削弱質(zhì)心向背景方向迭代的趨勢,提高目標識別的準確性。仿真實驗結(jié)果表明,本文提出的兩種算法都能改善目標跟蹤的準確性和穩(wěn)定性,且計算量增加不多,能夠滿足電視跟蹤系統(tǒng)實時性的要求。最后將本文提出的兩種改進跟蹤算法應(yīng)用到智能小車的跟蹤中,實驗表明本文提出的改進算法在實際應(yīng)用中可以獲得較好的跟蹤效果。
[Abstract]:Firstly, this paper introduces the development and research status of moving target recognition and tracking methods at home and abroad, and gives a brief description of the relevant algorithms. Then, the particle filter tracking algorithm and the Camshift tracking algorithm are introduced and explained in detail, and the two tracking algorithms are compared with each other in order to explain their characteristics and advantages and disadvantages. This paper analyzes the tracking environment of the two tracking algorithms. When the color difference between the target and the background is large, the particle filter tracking algorithm and the Camshift algorithm can effectively track the target. However, when the color difference between the target and the background is small or the target is in the complex background area, the target tracking will produce deviation, and even can not track the target accurately. In order to improve the stability and accuracy of the above two tracking algorithms in complex background, based on the two basic algorithms, their respective improved algorithms are proposed to improve their tracking performance in complex background. A particle filter tracking method based on significant histogram model is proposed. By comparing the distribution of pixel hue in the target and background region, the significance weights of different hue levels are determined, and the significance histogram model of the target is established. The significant histogram model can suppress the interference of the region with similar hue to the target recognition in the background and highlight the role of the significant hue of the target in target recognition so as to improve the accuracy of target recognition. A Camshift tracking algorithm based on edge suppression is proposed. By using the position and size of the object in the previous frame through the weight function, the brightness weight of the edge of the object is reduced in the reverse projection, and the suppressed edge can effectively distinguish the object from the background, and weaken the tendency of centroid iterating towards the background. Improve the accuracy of target recognition. The simulation results show that the two algorithms proposed in this paper can improve the accuracy and stability of target tracking, and the amount of computation is not much increased, which can meet the real-time requirements of TV tracking system. Finally, the two improved tracking algorithms proposed in this paper are applied to the tracking of intelligent cars. The experimental results show that the proposed improved tracking algorithm can obtain better tracking results in practical applications.
【學(xué)位授予單位】：天津工業(yè)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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