【摘要】：運動目標跟蹤是在連續(xù)變化的圖像序列中找出目標的位置和狀態(tài)的過程,而目標跟蹤實現(xiàn)的穩(wěn)定性和魯棒性則是要處理的主要問題。由于現(xiàn)實環(huán)境的復雜多樣性,一般算法對運動目標大小和形狀的改變適應性較差,不能有效的解決復雜情況和突變運動下目標跟蹤的問題,當目標發(fā)生遮擋、目標移動太快以及目標丟失等突變運動時不能自動恢復從而導致跟蹤失敗,很難實現(xiàn)目標跟蹤的準確性和穩(wěn)定性。為解決突變運動下的目標跟蹤問題,本文提出一種基于視覺顯著性的運動目標跟蹤算法,該算法將視覺注意機制運用到運動目標跟蹤框架中,利用時空顯著性算法對視頻序列進行檢測,生成視覺顯著圖,從視覺顯著圖對應的顯著性區(qū)域中建立目標的特征表示模型來實現(xiàn)運動目標的跟蹤。論文做了以下工作:(1)對運動目標跟蹤算法框架和視覺顯著性技術的理論基礎進行了闡述,并對目前常用的運動目標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進行了分析和實驗。(2)通過分析目前主流的時空顯著性檢測算法,有PQFT和SEG算法,并引入到運動目標跟蹤算法框架中,然后進行算法設計來對運動目標進行跟蹤。(3)采用國際公共視頻序列進行運動目標的跟蹤遮擋測試,旨在運動目標發(fā)生丟失、遮擋等突變運動情況下和復雜環(huán)境下能否準確和穩(wěn)定的跟蹤目標,并與目前主流目標跟蹤算法進行實驗對比和定量分析。實驗結果表明,本文方法在攝像機搖晃等動態(tài)場景下可以較準確檢測出時空均顯著的目標,有效克服了在運動目標發(fā)生丟失和遮擋等復雜和突變情況下跟蹤不穩(wěn)定問題,具有較強的魯棒性,從而實現(xiàn)復雜場景下目標較準確的跟蹤。
[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.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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