|Table of Contents|

Research of Scaling Parameter Optimization for Target Tracking(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年04期
Page:
69-76
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Research of Scaling Parameter Optimization for Target Tracking
Author(s):
Wu Huijun1; Li Meiyun1; Yang Wenyuan2
(1.School of Information Engineering,Zhangzhou Institute of Technology,Zhangzhou 363000,China)(2.Fujian Key Laboratory of Granular Computing and Application,Minnan Normal University,Zhangzhou 363000,China)
Keywords:
computer vision; target tracking; correlation filtering; scale estimation; parameter optimization
PACS:
TP391.4
DOI:
10.3969/j.issn.1001-4616.2019.04.010
Abstract:
Robust scale estimation has been a challenging problem in target tracking. In handling complex scale variation of the image sequence,existing algorithms have yet to be promoted in tracking speed and tracking precision. We build two related filters and introduce the scale transformation,and optimize the scale parameters of the target tracking,which can enhance the tracking speed and precision. First,this paper constructs a 1-dimensional correlation filter and a 2-dimensional correlation filter,and the 2-dimensional filter realizes the target tracking,determines the location of the object. The evaluation of scale transformation is realized by 1-dimensional filter. Then,the two filters are combined into a 3-dimensional filter to complete the detailed dimension space target positioning. Finally,we analyze the effect of scale factor on the tracking speed,centre location error,distance precision and overlap precision to obtain the optimized value. Experiments are performed on the data set OTB-2015,and the optimized value of target tracking scale parameters are acquired.

References:

[1] 毕笃彦,库涛,查宇飞,等. 基于颜色属性直方图的尺度目标跟踪算法研究[J]. 电子与信息学报,2016,38(5):1099-1106.
[2]蔡念,周杨,刘根,等. 鲁棒主成分分析的运动目标检测综述[J]. 中国图象图形学报,2018,21(10):1265-1275.
[3]HENRIQUES J F,CASEIRO R,MARTINS P,et al. High-speed tracking with kernelized correlation filters[J]. IEEE transactions on pattern analysis machine intelligence,2015,37(3):583-596.
[4]YUE Z,NARASIMHA L,PRAMOD T,et al. A robust real-time object detection and tracking system.[J]. Proceedings of SPIE—the international society for optical engineering,2008,6971:697108-1-697108-9.
[5]ROYER L,KRUPA A,DARDENNE G,et al. Real-time target tracking of soft tissues in 3D ultrasound images based on robust visual information and mechanical simulation[J]. Medical image analysis,2017,35:582-598.
[6]HENRIQUES J F,RUI C,MARTINS P,et al. Exploiting the circulant structure of tracking-by-detection with kernels[M]. Computer Vision:ECCV 2012. Berlin Heidelberg:Springer,2012.
[7]高文,朱明,贺柏根,等. 目标跟踪技术综述.[J]. 中国光学,2014,7(3):365-375.
[8]WU Y,LIM J,YANG M H. Online object tracking:a benchmark[C]//The IEEE Conference on Computer Vision & Pattern Recognition(CVPR),Portland,2013:.2411-2418.
[9]WU J,LI J,XIAO C,et al. Real-time robust algorithm for circle object detection:[C]//International Conference for Young Computer Scientists(ICYCS). Zhangjiajie,China. 2008:1722-1727.
[10]陈旭,孟朝晖. 基于深度学习的目标视频跟踪算法综述[J]. 计算机系统应用,2019,28(1):1-9.
[11]DANELLJAN M,KHAN F S,FELSBERG M,et al. Adaptive color attributes for real-time visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR),Columbus,OH,USA. 2014:1090-1097.
[12]GUAN M,LI Z,HE R,et al. High speed tracking with a fourier domain kernelized correlation filter[J]. arXiv preprint arXiv:03236,2018.
[13]ZHANG K,ZHANG L,LIU Q,et al. Fast visual tracking via dense spatio-temporal context learning[J]. European Conference on Computer Vision(ECCV),Zurich,Switzerland,2014:127-141.
[14]ZHANG K H,ZHANG L,YANG M H. Real-time object tracking via online discriminative feature selection[J]. IEEE transactions on image processing,2013,22(12):4664-4677.
[15]DING Z,LIU Y,LIU J,et al. Adaptive interacting multiple model algorithm based on information-weighted consensus for maneuvering target tracking[J]. Sensors,2018,18(7):2012-2021.
[16]ZHANG T Z,XU C S,YANG M H. Multi-task correlation particle filter for robust object tracking[C]//The IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu. HI,USA,2017:4335-4343.
[17]瞿中,赵从梅. 一种抗遮挡的自适应尺度目标跟踪算法[J]. 计算机科学,2018,45(4):296-300.
[18]BOLME D S,BEVERIDGE J R,DRAPER B A,et al. Visual object tracking using adaptive correlation filters[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR),San Francisco,CA,USA,2010:2544-2550.
[19]纪纲,高富东,范加利. 基于改进的MOSSE相关滤波的目标跟踪[J]. 计算机测量与控制,2018,237(6):244-247.
[20]ZHANG D,MAEI H,WANG X,et al. Deep reinforcement learning for visual object tracking in videos[J]. arXiv preprint arXiv:08936,2017.
[21]LI C,LIU X,SU X,et al. Robust kernelized correlation filter with scale adaption for real-time single object tracking[J]. Journal of real-time image processing,2018,15(4):583-596.
[22]CEHOVIN L,LEONARDIS A,KRISTAN M. Visual object tracking performance measures revisited[J]. IEEE transactions on image processing a publication of the IEEE signal processing society,2016,25(3):1261-1272.
[23]张雷,王延杰,孙宏海,等. 采用核相关滤波器的自适应尺度目标跟踪[J]. 光学精密工程,2016,24(2):448-459.
[24]ZHANG X,WANG Z,XIA G,et al. Accurate object tracking by combining correlation filters and keypoints[C]//International Joint Conference on Neural Networks(IJCNN),Vancouver,BC,Canada,2016:2522-2525.
[25]YANG R,WEI Z. Real-time visual tracking through fusion features[J]. Sensors,2016,16(7):949-958.
[26]李远状,韩彦芳,于书盼. 一种核相关滤波器的多尺度目标跟踪方法[J]. 电子科技,2018,31(10):5-9.
[27]陈倩茹,刘日升,樊鑫. 多相关滤波自适应融合的鲁棒目标跟踪.[J]. 中国图象图形学报,2018,23(2):269-276.
[28]ZHANG T,XU C S,YANG M H. Learning multi-task correlation particle filters for visual tracking[J]. IEEE transactions on pattern analysis and machine intelligence,2018,41(99):365-378.

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Last Update: 2019-12-31