【摘要】：運(yùn)動(dòng)目標(biāo)跟蹤是在連續(xù)變化的圖像序列中找出目標(biāo)的位置和狀態(tài)的過程,而目標(biāo)跟蹤實(shí)現(xiàn)的穩(wěn)定性和魯棒性則是要處理的主要問題。由于現(xiàn)實(shí)環(huán)境的復(fù)雜多樣性,一般算法對(duì)運(yùn)動(dòng)目標(biāo)大小和形狀的改變適應(yīng)性較差,不能有效的解決復(fù)雜情況和突變運(yùn)動(dòng)下目標(biāo)跟蹤的問題,當(dāng)目標(biāo)發(fā)生遮擋、目標(biāo)移動(dòng)太快以及目標(biāo)丟失等突變運(yùn)動(dòng)時(shí)不能自動(dòng)恢復(fù)從而導(dǎo)致跟蹤失敗,很難實(shí)現(xiàn)目標(biāo)跟蹤的準(zhǔn)確性和穩(wěn)定性。為解決突變運(yùn)動(dòng)下的目標(biāo)跟蹤問題,本文提出一種基于視覺顯著性的運(yùn)動(dòng)目標(biāo)跟蹤算法,該算法將視覺注意機(jī)制運(yùn)用到運(yùn)動(dòng)目標(biāo)跟蹤框架中,利用時(shí)空顯著性算法對(duì)視頻序列進(jìn)行檢測,生成視覺顯著圖,從視覺顯著圖對(duì)應(yīng)的顯著性區(qū)域中建立目標(biāo)的特征表示模型來實(shí)現(xiàn)運(yùn)動(dòng)目標(biāo)的跟蹤。論文做了以下工作:(1)對(duì)運(yùn)動(dòng)目標(biāo)跟蹤算法框架和視覺顯著性技術(shù)的理論基礎(chǔ)進(jìn)行了闡述,并對(duì)目前常用的運(yùn)動(dòng)目標(biāo)跟蹤算法有均值漂移法、粒子濾波法、卡爾曼濾波法,和視覺顯著性檢測算法有Itti、CA、SR、LC進(jìn)行了分析和實(shí)驗(yàn)。(2)通過分析目前主流的時(shí)空顯著性檢測算法,有PQFT和SEG算法,并引入到運(yùn)動(dòng)目標(biāo)跟蹤算法框架中,然后進(jìn)行算法設(shè)計(jì)來對(duì)運(yùn)動(dòng)目標(biāo)進(jìn)行跟蹤。(3)采用國際公共視頻序列進(jìn)行運(yùn)動(dòng)目標(biāo)的跟蹤遮擋測試,旨在運(yùn)動(dòng)目標(biāo)發(fā)生丟失、遮擋等突變運(yùn)動(dòng)情況下和復(fù)雜環(huán)境下能否準(zhǔn)確和穩(wěn)定的跟蹤目標(biāo),并與目前主流目標(biāo)跟蹤算法進(jìn)行實(shí)驗(yàn)對(duì)比和定量分析。實(shí)驗(yàn)結(jié)果表明,本文方法在攝像機(jī)搖晃等動(dòng)態(tài)場景下可以較準(zhǔn)確檢測出時(shí)空均顯著的目標(biāo),有效克服了在運(yùn)動(dòng)目標(biāo)發(fā)生丟失和遮擋等復(fù)雜和突變情況下跟蹤不穩(wěn)定問題,具有較強(qiáng)的魯棒性,從而實(shí)現(xiàn)復(fù)雜場景下目標(biāo)較準(zhǔn)確的跟蹤。
[Abstract]:Moving target tracking is the process of finding out the position and state of the target in a continuously changing image sequence, and the stability and robustness of target tracking are the main problems to be dealt with. Because of the complexity and diversity of the real environment, the general algorithm has poor adaptability to the change of the size and shape of the moving object, so it can not effectively solve the problem of target tracking under the complex situation and sudden motion. It is difficult to achieve the accuracy and stability of target tracking because it can not recover automatically when the target moves too fast or when the target is lost. In order to solve the problem of moving target tracking under sudden motion, a moving target tracking algorithm based on visual saliency is proposed in this paper, which applies visual attention mechanism to moving target tracking framework. Using spatio-temporal salience algorithm to detect video sequence and generate visual saliency map, the target feature representation model is established from the salience region corresponding to visual salience map to achieve moving target tracking. The following works are done in this paper: (1) the frame of moving target tracking algorithm and the theoretical basis of visual salience technology are expounded. The commonly used moving target tracking algorithms are mean shift method, particle filter method, Kalman filter method, and so on. And visual salience detection algorithms are analyzed and experimented. (2) by analyzing the current mainstream spatio-temporal salience detection algorithms, there are PQFT and SEG algorithms, which are introduced into the framework of moving target tracking algorithm. Then the algorithm is designed to track the moving target. (3) the international common video sequence is used to track the moving object in order to lose the moving target. Whether the target can be tracked accurately and stably under the condition of sudden motion such as occlusion and complex environment, and compared with the current mainstream target tracking algorithm, the experiment and quantitative analysis are carried out. The experimental results show that the proposed method can accurately detect spatio-temporal targets in dynamic scenes such as camera shaking, and can effectively overcome the problem of tracking instability in complex and abrupt situations such as loss and occlusion of moving targets. It has strong robustness so that the target can be tracked accurately in complex scene.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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