本文選題：目標跟蹤 + 卷積特征　； 參考：《中國圖象圖形學報》2017年09期

【摘要】：目的針對現(xiàn)實場景中跟蹤目標的快速運動、旋轉、尺度變化、遮擋等問題,提出了基于卷積特征的核相關自適應目標跟蹤的方法。方法利用卷積神經網絡提取高、低層卷積特征并結合本文提出的核相關濾波算法計算并獲得高底兩層卷積特征響應圖。采用Coarse-to-Fine方法對目標位置進行估計,在學習得到1維尺度核相關濾波器估計尺度的基礎上實時更新高低兩層核相關濾波器參數(shù),以實現(xiàn)自適應的目標跟蹤。結果實驗選取公開數(shù)據集中的典型視頻序列進行跟蹤,測試了算法在目標尺度發(fā)生變化、遮擋、旋轉等復雜場景下的跟蹤性能并與多種優(yōu)秀的跟蹤算法在平均中心誤差、平均重疊率等指標上進行了定量比較,在Singer1、Car4、Jogging、Girl、Football以及MotorRolling視頻圖像序列上的中心誤差分別為8.71、6.83、3.96、3.91、4.83、9.23,跟蹤重疊率分別為0.969、1.00、0.967、0.994、0.967、0.512。實驗結果表明,本文算法與原始核相關濾波算法相比,平均中心位置誤差降低20%,平均重疊率提高12%。結論采用卷積神經網絡提取高低兩層卷積特征,高層卷積特征用于判別目標和背景,低層卷積特征用于預測目標位置并通過Coarse-to-Fine方法對目標位置進行精確的定位,較好地解決了由于目標的旋轉和尺度變化帶來的跟蹤誤差大的問題,提高了跟蹤性能并能夠實時更新學習。在目標尺度發(fā)生變化、遮擋、光照條件改變、目標快速運動等復雜場景下仍表現(xiàn)出較強的魯棒性和適應性。
[Abstract]:Aim to solve the problems of fast moving, rotation, scale change, occlusion and so on, a kernel correlation adaptive target tracking method based on convolution feature is proposed. Methods the high and low level convolution features were extracted by convolution neural network and the response map of the high bottom two-layer convolution feature was obtained by combining the kernel correlation filtering algorithm proposed in this paper. The Coarse-to-Fine method is used to estimate the location of the target. Based on the learning of the estimation scale of the 1-dimensional kernel correlation filter, the parameters of the high and low two layers correlation filter are updated in real time to achieve adaptive target tracking. Results the typical video sequences in the open data set were selected for tracking, and the tracking performance of the algorithm in complex scenes such as target scale change, occlusion, rotation and so on was tested, and the average center error of the algorithm was compared with that of many excellent tracking algorithms. Quantitative comparison was made on the average overlap rate. The central errors in Singer 1 / Car4 Jogging GirlsFootball and MotorRolling video sequences were 8.71 / 6.83 / 3.963.91 / 4.839.23, respectively, and the tracking overlap rates were 0.9691.000.9670.9940.967/ 0.967/ 0.512, respectively. The experimental results show that compared with the original kernel correlation filtering algorithm, the average center position error is reduced by 20% and the average overlap rate is increased by 12%. Conclusion the convolution neural network is used to extract the convolution feature of high and low layers, the high level convolution feature is used to distinguish the target and the background, and the lower level convolution feature is used to predict the target position and to locate the target position accurately by Coarse-to-Fine method. The problem of large tracking error caused by target rotation and scale change is well solved, and the tracking performance is improved and the learning can be updated in real time. It still shows strong robustness and adaptability in complex scenes such as the change of target scale, occlusion, light condition change, fast moving of target and so on.
【作者單位】： 中北大學計算機與控制工程學院;
【基金】：山西省自然科學基金項目(2013011017-6)~~
【分類號】：TP391.41

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