计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 224-230.doi: 10.11896/jsjkx.200500084
马康1, 娄静涛2, 苏致远1, 李永乐2, 朱愿2
MA Kang1, LOU Jing-tao2, SU Zhi-yuan1, LI Yong-le2, ZHU Yuan2
摘要： 在目标跟踪过程中,改进尺度自适应策略、选择辨别能力强的特征是提高跟踪算法性能的重要途径。为解决核相关滤波算法(Kernel Correlation Filtering,KCF)不能适应目标尺度变化、采用单一的方向梯度直方图(Histogram of Oriented Gra-dient,HOG)特征对目标判别能力有限的问题,通过研究同一目标在不同尺度下相关响应值的大小,在分析大量统计数据的基础上发现其变化规律,提出了一种新的尺度自适应策略,并采取HOG和颜色属性特征(Color Name,CN)线性加权融合的方法提高对目标的判别能力。在OTB数据集上的实验结果表明,所提算法的准确率和成功率相比KCF算法分别提高了8.5%和28.9%,在尺度变化属性视频序列上的准确率和成功率相比KCF算法分别提高了8.1%和38.5%,在其他属性视频序列上的表现也有较大提高,并且跟踪速度达到37.68 fps,可满足实时性要求。
中图分类号:
| [1] MENG L,YANG X.A survey of object tracking algorithms[J].Acta Automatica Sinica,2019,45(7):1244-1260. [2] LIU Q Y,WANG Y R,ZHANG J L,et al.Research progress of visual tracking methods based on correlation filter[J].Acta Auto-matica Sinica,2019,45(2):265-275. [3] BOLME D S,BEVERIDGE J R,DRAPER B A,et al.Visual object tracking using adaptive correlation filters[C]//2010 IEEEComputer Society Conference on Computer Vision and PatternRecognition.IEEE,2010:2544-2550. [4] HENRIQUESJF C R.Exploiting the circulant structure oftracking-by-detection with kernels [C]//ECCV.2012:702-715. [5] JIANG X F,HAN Q L.On designing fuzzy controllers for aclass of nonlinear networked control systems[J].IEEE Transactions on Fuzzy Systems,2008,16(4):1050-1060. [6] HENRIQUES J F,CASEIRO R,MARTINS P,et al.High-Speed Tracking with Kernelized Correlation Filters.[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):583-596. [7] DANELLJAN M,HÄGER G,KHAN F S,et al.Discriminative scale space tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,39(8):1561-1575. [8] DANELLJAN M,HÄGER G,KHAN F,et al.Accurate scaleestimation for robust visual tracking [C]//British Machine Vision Conference.BMVA Press,2014. [9] LI Y,ZHU J.A scale adaptive kernel correlation filter tracker with feature integration[C]//European Conference on Compu-ter Vision.Springer,Cham,2014:254-265. [10] DANELLJAN M,SHAHBAZ K F,FELSBERG M,et al.Adaptive color attributes for real-time visual tracking[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:1090-1097. [11] WU Y,LIM J,YANG M H.Online object tracking:A benchmark [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2013:2411-2418. |
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