【摘要】：近年來目標跟蹤算法取得了顯著的成果,但由于實際跟蹤過程中受到復雜背景及目標的多變性等影響,目標跟蹤仍具有一定的挑戰(zhàn)性。因此,本文在核相關濾波目標跟蹤算法上討論了其優(yōu)缺點并進行了研究,主要創(chuàng)新如下:(1)為了解決目標跟蹤過程光照變化,跟蹤不準確的問題,提出了一種基于視覺特性的核相關濾波跟蹤方法。首先,利用顏色屬性作為目標特征,從而提高跟蹤器的光照不敏感性;然后,采用局部線性嵌入方法自適應降維,以達到低維特征空間;最后,根據正則化最小二乘分類器獲得目標位置。實驗表明,所提算法具有良好的光照不敏感性,且在復雜背景下具有更好的跟蹤性能。(2)為了解決目標跟蹤過程中尺度變化和遮擋的問題,提出了一種抗遮擋的目標跟蹤算法。引入一個多尺度濾波器,根據濾波器的響應最大值進行尺度預測;并根據目標位置峰值尖銳度的差異性,正確更新模型。實驗表明,所提算法在復雜背景下能有效地解決目標尺度變化、部分或完全遮擋等問題,綜合性能有了明顯的提升。(3)為了解決目標跟蹤過程中快速運動、運動模糊的問題,提出了一種自適應目標響應的核相關濾波跟蹤算法。在核相關濾波跟蹤框架上,加入一項先驗目標響應來聯(lián)合優(yōu)化訓練分類器,使得學習的分類器可以解決循環(huán)移位造成的邊界效應;當響應值小于設定閾值時,利用在線支持向量機(SVM)分類器對目標進行重定位檢測,提高了算法的魯棒性。實驗表明,本文算法在目標發(fā)生快速運動、運動模糊等問題情況下,能準確、可靠地跟蹤目標。本文圍繞核相關濾波目標跟蹤算法中面臨的難點問題,展開了深入研究,并提出了相應的改進方法。實驗結果表明,本文所提的三種算法分別在光照變化、遮擋、尺度變化及快速運動上,具有良好的跟蹤性能。
[Abstract]:In recent years, the target tracking algorithm has achieved remarkable results. However, due to the complex background and the variability of the target in the actual tracking process, target tracking is still challenging. Therefore, in this paper, the advantages and disadvantages of the kernel correlation filter target tracking algorithm are discussed and studied. The main innovations are as follows: (1) in order to solve the problem of inaccurate tracking in the process of target tracking, A kernel correlation filter tracking method based on visual characteristics is proposed. Firstly, the color attribute is used as the target feature to improve the illumination insensitivity of the tracker. Then, the local linear embedding method is used to reduce the dimension adaptively to achieve the low dimensional feature space. The target position is obtained according to the regularized least square classifier. Experiments show that the proposed algorithm has good illumination insensitivity and better tracking performance in complex background. (2) in order to solve the problem of the change of scale and occlusion in the process of target tracking, an anti-occlusion target tracking algorithm is proposed. A multi-scale filter is introduced to predict the scale according to the maximum response of the filter, and the model is updated correctly according to the difference of the peak sharpness of the target position. Experiments show that the proposed algorithm can effectively solve the problems of target scale change, partial or complete occlusion and so on. (3) in order to solve the problem of fast motion and motion blur in the process of target tracking, the proposed algorithm can effectively solve the problems such as the change of target scale, partial or complete occlusion and so on. A kernel correlation filter tracking algorithm for adaptive target response is proposed. In the framework of kernel correlation filter tracking, a priori target response is added to optimize the training classifier, so that the learning classifier can solve the boundary effect caused by cyclic shift, and when the response value is less than the threshold value, The online support vector machine (SVM) classifier is used to detect the target location, which improves the robustness of the algorithm. The experiments show that the algorithm can track the target accurately and reliably under the condition of fast motion and motion blur. This paper focuses on the difficult problems in the kernel correlation filter target tracking algorithm, and puts forward the corresponding improvement methods. The experimental results show that the three algorithms proposed in this paper have good tracking performance on illumination variation, occlusion, scale change and fast motion.
【學位授予單位】：華僑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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