【摘要】：盡管經(jīng)過多年的研究,尺度變化、形狀變化、嚴重的遮擋、背景干擾、光照變化和相機運動等內(nèi)外因素引起的目標表觀變化,使得目標跟蹤仍然是一個極具挑戰(zhàn)的問題.為了有效地處理目標表觀變化,基于分層卷積特征和尺度自適應核相關濾波器的目標跟蹤算法,將目標跟蹤分解為目標位置的預測和尺度的估計兩個步驟.在目標位置估計方面,區(qū)別于傳統(tǒng)的基于手工設計特征的目標跟蹤算法,我們使用基于分層卷積特征的相關濾波器算法計算出不同卷積層上的跟蹤結果置信圖,對各個層上得到的結果進行加權求和得到目標置信圖,估計出目標的最終位置.在目標的尺度估計方面,為了有效捕捉目標尺度變化,我們首先使用尺度金字塔對下一幀適用的尺度進行預測,同時對目標尺度進行更新.在標準測試集(OTB-50)上的實驗結果表明,本文所提出的融合分層卷積特征和尺度自適應的相關濾波器的目標跟蹤算法取得較好的精度和魯棒性.
[Abstract]:After years of research, target tracking is still a challenging problem due to the external and internal factors such as scale change, shape change, severe occlusion, background interference, illumination change and camera motion. In order to deal with the target apparent change effectively, the target tracking algorithm based on hierarchical convolution feature and scale adaptive kernel correlation filter decomposes the target tracking into two steps: prediction of target location and estimation of scale. In the aspect of target location estimation, different from the traditional target tracking algorithm based on manual design features, we use the correlation filter algorithm based on hierarchical convolution feature to calculate the confidence chart of tracking results on different convolution layers. The final position of the target is estimated by weighted summation of the results at each level. In the aspect of target scale estimation, in order to capture the change of target scale effectively, we first use the scale pyramid to predict the scale applicable to the next frame and update the target scale at the same time. The experimental results on the standard test set (OTB-50) show that the proposed target tracking algorithm based on hierarchical convolution feature and scale adaptive correlation filter achieves good accuracy and robustness.
【作者單位】： 華僑大學計算機科學與技術學院;華僑大學計算機視覺與模式識別重點實驗室;
【基金】：華僑大學研究生科研創(chuàng)新能力培育計劃項目(1511314014)資助 國家自然科學基金面上項目(61572205)資助 福建省自然科學基金項目(2015J01257)資助 華僑大學科技創(chuàng)新能力提升“中青年教師科技創(chuàng)新”計劃項目(ZQN-PY210)資助
【分類號】：TN713;TP391.41
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本文編號：2284991