【摘要】：目標跟蹤在現(xiàn)代機器視覺起著重要作用。最近基于核相關(guān)濾波的跟蹤達到很好的效果,但仍有改進的必要,本文對其進行了全面分析,提出兩個改進方案。為了解決遮擋引起的跟蹤丟失問題,建立一個目標檢測模塊。首先通過對當前一些檢測算法的分析,確定檢測方案。通過提取目標模板與當前幀的SURF特征點進行匹配,運用改進的RANSAC算法過濾匹配對,計算變換矩陣,來定位當前幀的目標位置,以達到檢測目的。同時建立模板集合來增加魯棒性。建立了判斷機制,通過在當前幀訓練跟蹤器,進行反向跟蹤,然后比較結(jié)果判斷是否啟動檢測,同時修改了模板更新方法。針對核濾波跟蹤無法適應目標尺度變化的問題,通過引入目標候選框算法來產(chǎn)生尺度不同的方框。通過結(jié)構(gòu)化隨機森林引出提取目標候選框的edge box算法,同時修改了算法以適應需要。用原算法進行粗跟蹤,在結(jié)果位置處提取區(qū)域,在該區(qū)域運行edge box以產(chǎn)生目標候選方框,將一些評分高的方框提取出來,結(jié)合原跟蹤框進行篩選后,再變換回初始大小,然后代入KCF進行評估,綜合原結(jié)果得出當前幀最合適的跟蹤框。同時融合了多種特征以進一步提高整體跟蹤效果。本文同時論述了跟蹤評估方式,評估兩種改進方案時從OTB數(shù)據(jù)集中分別選取29個遮擋屬性的視頻與28個尺度屬性視頻,分別通過定性與定量實驗與原算法以及Stuck與TLD進行了對比。實驗結(jié)果表明,本算法在成功率圖與精確度圖排名上均優(yōu)于原KCF,TLD,struck算法。與原方法相比,改進后的方法能更好地適用于有尺度變化與遮擋的跟蹤,能夠廣泛應用于目標跟蹤領(lǐng)域。
[Abstract]:Target tracking plays an important role in modern machine vision. Recently, the tracking based on kernel correlation filter has achieved good results, but there is still a need for improvement. This paper makes a comprehensive analysis of it and puts forward two improved schemes. In order to solve the problem of tracking loss caused by occlusion, a target detection module is established. First of all, through the analysis of some current detection algorithms, the detection scheme is determined. By extracting the target template to match the SURF feature points of the current frame, the improved RANSAC algorithm is used to filter the matching pairs, and the transform matrix is calculated to locate the target position of the current frame so as to achieve the purpose of detection. At the same time, the template set is established to increase robustness. A judgment mechanism is established, and the method of template updating is modified by training the tracker in the current frame for reverse tracking, then comparing the results to judge whether the detection is initiated or not. In order to solve the problem that the kernel filter tracking can not adapt to the change of target scale, the target candidate algorithm is introduced to generate different scale boxes. The edge box algorithm for extracting target candidate is obtained by structured random forest, and the algorithm is modified to meet the needs. The original algorithm is used for rough tracking, the region is extracted at the result location, the edge box is run in this region to produce the target candidate box, and some highly graded boxes are extracted. After the original tracking box is filtered, the initial size is then transformed back to the initial size. Then the KCF is used to evaluate the current frame and the most suitable tracking box is obtained by synthesizing the original results. At the same time, a variety of features are fused to further improve the overall tracking effect. At the same time, this paper discusses the method of tracking and evaluation. When evaluating the two improved schemes, the video of 29 occlusion attributes and 28 scale attribute videos are selected from the OTB dataset, respectively. The qualitative and quantitative experiments are compared with the original algorithm, and the Stuck and TLD are compared. The experimental results show that the proposed algorithm is superior to the original KCFC TLDLD-struck algorithm in the ranking of the success rate diagram and the accuracy chart. Compared with the original method, the improved method is more suitable for tracking with scale variation and occlusion, and can be widely used in the field of target tracking.
【學位授予單位】：西南科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：2195162