特征融合的核相关滤波跟踪算法
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摘要
针对目标跟踪过程中出现的形变、遮挡等干扰问题,通过结合基于模板匹配的核相关滤波算法(KCF)与颜色直方图统计模型,提出一种基于特征融合的尺度自适应的核相关滤波目标跟踪算法。首先分别训练KCF位置滤波器、前景与背景颜色模型以及尺度滤波器,检测时将得到的模板响应图与颜色统计概率图线性叠加,得到最终响应图,求得目标位置,然后根据尺度滤波器得到的响应图得到目标尺度。实验结果表明,该算法对形变、遮挡、尺度变化、光照变化、运动模糊、旋转等问题都能得到比较鲁棒的结果,在OTB数据集上的实验结果明显好于KCF和DSST,精确度为72.8%,稳定度为70.87%。
Aiming at the interference and occlusion problems in the target tracking process,a kernel-based filtering target tracking based on feature fusion is proposed by combining the kernel correlation filtering algorithm(KCF) based on template matching and color histogram statistical model.algorithm.Firstly,training the KCF position filter,the foreground and background color models,and the scale filter.The template response map and the color statistical probability map are linearly superimposed on the detection to obtain the final response map,get the target position,and then according to response map obtained from the scale filter gets the target scale.The experimental results show that this algorithm can obtain robust results for deformation,occlusion,scale change,illumination variation,motion blur,rotation,etc.,and the tracking results are significantly better than KCF and DSST.
Aiming at the interference and occlusion problems in the target tracking process,a kernel-based filtering target tracking based on feature fusion is proposed by combining the kernel correlation filtering algorithm(KCF) based on template matching and color histogram statistical model.algorithm.Firstly,training the KCF position filter,the foreground and background color models,and the scale filter.The template response map and the color statistical probability map are linearly superimposed on the detection to obtain the final response map,get the target position,and then according to response map obtained from the scale filter gets the target scale.The experimental results show that this algorithm can obtain robust results for deformation,occlusion,scale change,illumination variation,motion blur,rotation,etc.,and the tracking results are significantly better than KCF and DSST.
引文
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[2]KALMAN R E.A new approach to linear filtering and prediction problems[J].Journal of Basic Engineering Transactions,1960(82):35-45.
[3]NUMMIARO K,KOLLER-MEIER E,GOOL L V.An adaptive color-based particle filter[J].Image&Vision Computing,2003,21(1):99-110.
[4]HARE S,SAFFARI A,TORR P H S.Struck:Structured output tracking with kernels[J].IEEETransactions on Pattern Analysis&Machine Intelligence,2015,38(10):2096-2109.
[5]KALAL Z,MIKOLAJCZYK K,MATAS J.Tracking-learning-detection.[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2012,34(7):1409-22.
[6]BOLME D S,BEVERIDGE J R,DRAPER B A,et al.Visual object tracking using adaptive correlation filters[C].Computer Vision and Pattern Recognition,IEEE,2010:2544-2550.
[7]RUI C,MARTINS P,BATISTA J.Exploiting the circulant structure of tracking-by-detection with kernels[C].European Conference on Computer Vision,Springer-Verlag,2012:702-715.
[8]HENRIQUES J F,RUI C,MARTINS P,et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2014,37(3):583-596.
[9]DANELLJAN M,KHAN F S,FELSBERG M,et al.Adaptive color attributes for real-time visual tracking[C].IEEE Conference on Computer Vision and Pattern Recognition,IEEE Computer Society,2014:1090-1097.
[10]DANELLJAN M,HGER G,KHAN F S,et al.Accurate scale estimation for robust visual tracking[C].British Machine Vision Conference,2014:1-65.
[11]DANELLJAN M,HAGER G,KHAN F S,et al.Learning spatially regularized correlation filters for visual tracking[C].IEEE International Conference on Computer Vision,2016:4310-4318.
[12]BERTINETTO L,VALMADRE J,GOLODETZ S,et al.Staple:Complementary learners for real-time tracking[C].Computer Vision and Pattern Recognition,IEEE,2016:1401-1409.
[13]POSSEGGER H,MAUTHNER T,BISCHOF H.In defense of color-based model-free tracking[C].Computer Vision and Pattern Recognition,IEEE,2015:2113-2120.
[14]WU Y,LIM J,YANG M H.Online object tracking:A benchmark[C].IEEE Conference on Computer Vision and Pattern Recognition,IEEE Computer Society,2013:2411-2418..