计算机科学 ›› 2016, Vol. 43 ›› Issue (1): 306-309.doi: 10.11896/j.issn.1002-137X.2016.01.066

• 图形图像与模式识别 • 上一篇    下一篇

在线矩形特征选择的压缩跟踪算法

曹义亲,程威,黄晓生   

  1. 华东交通大学软件学院 南昌330013,华东交通大学软件学院 南昌330013,华东交通大学软件学院 南昌330013
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(61365008),江西省自然科学基金项目(20142BAB207025)资助

Compression Tracking Algorithm for Online Rectangle Feature Selection

CAO Yi-qin, CHENG Wei and HUANG Xiao-sheng   

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对压缩跟踪算法无法选择合适的矩形特征,易出现目标漂移、丢失现象,提出了一种基于在线矩形特征选择的压缩跟踪算法。首先,在初始化阶段生成投影矩阵,利用该投影矩阵提取特征来构造候选特征池,在特征池中使用矩形特征来表示目标特性,并去除与目标差异较大的矩形特征,最后计算分类分数最大的窗口,并将其作为目标窗口,从而实现跟踪。实验结果表明,该算法特征总数量比压缩跟踪算法特征总数量减少了13%,且跟踪精度和鲁棒性方面得到了改善,对于320pixel×240pixel大小的视频平均处理帧速为20frame/s,满足实时性要求。

关键词: 压缩感知,在线矩形特征选择,压缩跟踪,特征池

Abstract: Compressive tracking algorithm can not select appropriate object futures which will result in drifting or make tracking not accurate when the object is occluded or its appearance changes.To address this problem,this paper proposed a real-time compressive tracking algorithm based on rectangle feature selection.Firstly,generate projection matrixes are generated in an initial phase.And the projection matrixes are used to extract the feature to construct a feature pool.The rectangle feature is used to represent the characteristics of target in the feature pool, and the rectangular features with greater difference from the target characteristics are removed.Finally,the classifier is taken to process candidate samples by Bayes classification and response results to the classifier are taken as tracking results.The experimental results show that the proposed algorithm is about 13% lower than that of compressive tracking.It improves the trac-king accuracy and robustness,and the processing frame rate is 20 frame/s on a 320pixel×240pixel video sequence,which meets the requirements of real-time tracking.

Key words: Compressive sensing,Online rectangle feature selection,Compressive tracking,Feature pool

[1] Hare S,Saffari A,Torr P H S.Struck:structured output trac-king with kernels[C]∥Proceedings of IEEE International Conference on Computer Vision.Barcelona:IEEE,2011:263-270
[2] Li H X,Shen CH H,Shi Q F.Real-time visual tracking using compressive sensing[C]∥2011 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2011:1305-1312
[3] Zhang K H,Zhang Lei,Ming-Hsuan Y.Real time compressive tracking [C]∥Proceedings of European Conference on Compu-ter Vision,PartII.,2012:866-879
[4] Collins R T,Liu Y,Leordeanu M.Online selection of discriminative tracking features[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1631-1643
[5] Liang D,Huang Q,Jiang S,et al.Mean-shift blob tracking with adaptive feature selection and scale adaptation[C]∥IEEE International Conference on Image Processing,2007(ICIP 2007).IEEE,2007:369-372
[6] Zhu Q P,Yan J,Zhang H,et al.Real-time tracking using multiple features based on compressive sensing[J].Opt.Precision Eng,2013(2):437-444(in Chinese)朱秋平,颜佳,张虎,等.基于压缩感知的多特征实时跟踪[J].光学精密工程,2013(2):437-444
[7] Mao Zheng,Yuan Jian-jian,Wu Zhen-rong,et al.Real-time compressive tracking based on online feature selection[J].Opt.Precision Engineering,2014,2(3):730-736(in Chinese) 毛征,袁建建,吴珍荣,等.基于在线特征选择的实时压缩跟踪[J].光学精密工程,2014,2(3):730-736
[8] Zhong Quan,Zhou Jin,Wu Qin-zhang,et al.An Improved Real-time Compressive Tracking[J].Opt.Opto-Electronic Enginee-ring,2014(4):1-8(in Chinese)钟权,周进,吴钦章,等.一种改进的实时压缩跟踪算法[J].光电工程,2014(4):1-8
[9] Shi W Z,Ning J F,Yan Y F.Feature selection and target model updating in compressive tracking[J].Joumal of Image and Graphics,2014,19(6):932-939(in Chinese)石武祯,宁纪锋,颜永丰.压缩感知跟踪中的特征选择与目标模型更新[J].中国图象图形学报,2014,19(6):932-939
[10] Luo H L,Zhong B K,Kong F S.Object tracking algorithm by combining the predicted target position with compressive trac-king[J].Journal of Image and Graphics,2014,19(6):875-885(in Chinese) 罗会兰,钟宝康,孔繁胜.结合目标预测位置的压缩跟踪[J].中国图象图形学报,2014,19(6):875-885
[11] Jiao Li-cheng,Yang Shu-yuan,Liu Fang,et al.Development and prospect of compressive sensing[J].Acta Electronica Sinica,2011,39(7):1651-1662(in Chinese)焦李成,杨淑媛,刘芳,等.压缩感知回顾与展望[J].电子学报,2011,39(7):1651-1662
[12] Dai Q H,Fu C J,Ji X Y.Research on compressed sensing[J].Chinese Journal of Computers,2011,34(3):425-434(in Chinese)戴琼海,付长军,季向阳.压缩感知研究[J].计算机学报,2011,4(3):425-434
[13] Achlioptas D.Database-friendly random projections:Johnson-Lindenstrauss with binary coins[J].Journal of computer and System Sciences,2003,66(4):671-687
[14] Diaconis P,Freedman D.Asymptotics of graphical projectionpursuit [J].Annals of Statistics,1984,12(3):228-235
[15] Papageorgiou C P,Oren M,Poggio T.A general framework for object detection[C]∥Sixth International Conference on Computer Vision,1998.IEEE,1998:555-562
[16] Viola P,Jones M J.Robust real-time face detection[J].International Journal of Computer Vision,2004,57(2):137-154
[17] Cao Ying,Miao Qi-guang,Liu Jia-chen,et al.Advance and Prospects of AdaBoost Algorithm[J].Acta Automatica Sinica,2013,39(6):745-758(in Chinese)曹莹,苗启广,刘家辰,等.AdaBoost算法研究进展与展望[J].自动化学报,2013,9(6):745-758
[18] Zhou S R,Yin J P.LBP texture feature based on Haar characteristics[J].Journal of Software,2013,4(8):1909-1926(in Chinese)周书仁,殷建平.基于Haar特性的LBP纹理特征[J].软件学报,2013,4(8):1909-1926

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