Journal of Guangxi Normal University(Natural Science Edition) ›› 2018, Vol. 36 ›› Issue (4): 42-50.doi: 10.16088/j.issn.1001-6600.2018.04.006

Previous Articles     Next Articles

Infrared-Visible Target Tracking Basedon AdaBoost Confidence Map

ZHANG Canlong1,2*, SU Jiancai1,2, LI Zhixin1,2, WANG Zhiwen3   

  1. 1. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, GuilinGuangxi 541004,China;
    2.Guangxi Collaborative Innovation Center of Multi-source Information Integration andIntelligent Processing, Guilin Guangxi 541004, China;
    3. College of Computer Science and CommunicationEngineering, Guangxi University of Science and Technology, Liuzhou Guangxi 545006, China
  • Received:2018-01-01 Published:2018-10-20

PDF (PC)

19

Abstract: To address the problem that the tracker is easy to drift away from the target and even failure in complex scenes, this paper presents an infrared-visible target tracking algorithm based on AdaBoost confidence map. Firstly, the target samples and background samples in infrared-visible images are characterized by using color and texture descriptor and are classified using AdaBoost classifier, and then the confidence maps of infrared and visible images are calculated based on the classification scores. Secondly, the similarity between confidence maps of target candidate and its template is calculated for visible and infrared images, and the visible similarity and infrared similarity are integrated into a joint objective function by weighting. Finally, a joint target location-shift formula is induced by performing multi-variable Taylor series expansion and maximization on the objective function, and the optimal target location is gained recursively by applying the mean shift procedure. The experimental result in infrared-visible image sequences demonstrates that the proposed method performs well in dealing with illumination change, target intersection, target occlusion and so on.

Key words: AdaBoost classifier, confidence map, infrared-visible target, fusion tracking

CLC Number: 

  • TP391.41
[1] HARE S, SAFFARI A, TORR P H S.Struck: structured output tracking with kernels[C]//2011 International Conference on Computer Vision.Piscataway,NJ:IEEE Press,2011:263-270. DOI:10.1109/ICCV.2011.6126251.
[2] PANG Yu,LING Haibin.Finding the best from the second bests:inhibiting subjective bias in evaluation of visual tracking algorithms[C]//2013 IEEE International Conference on Computer Vision.Piscataway,NJ:IEEE Press,2013:2784-2791.DOI:10.1109/ICCV.2013.346.
[3] XING Junliang,GAO Jin,LI Bing,et al.Robust object tracking with online multi-lifespan dictionary learning[C]//2013 IEEE International Conference on Computer Vision. Piscataway,NJ:IEEE Press,2013:665-672.DOI:10.1109/ICCV.2013.88.
[4] BAI Qinxun,WU Zheng,SCLAROFF S,et al.Randomized ensemble tracking[C]//2013 IEEE International Conference on Computer Vision.Piscataway,NJ:IEEE Press, 2013:2040-2047. DOI:10.1109/ICCV.2013.255.
[5] 程咏梅,王进行,魏坤,等.基于加权mean-shift可见光/红外双通道目标跟踪[J].光子学报,2010,39(2):357-363.DOI:10.3788/gzxb20103902.0357.
[6] 张灿龙,唐艳平,李志欣,等.红外可见光目标的空间直方图表示与联合跟踪[J].中国图象图形学报,2017,22(4):492-501.DOI:10.11834/jig.20170409.
[7] 蔡冰,张灿龙,李志欣.基于联合直方图的红外与可见光目标融合跟踪[J].广西师范大学学报(自然科学版),2017,35(3):37-44.DOI:10.16088/j.issn.1001-6600.2017.03.005.
[8] BAI Yancheng,TANG Ming.Robust tracking via weakly supervised ranking SVM[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway,NJ:IEEE Press,2012: 1854-1861.DOI:10.1109/CVPR.2012.6247884.
[9] FREUND Y,SCHAPIRE R E.Experiments with a new boosting algorithm[C]//Machine Learning: Proceedings of the Thirteenth International Conference.San Francisco,CA:Morgan Kaufmann Publishers,1996:148-156.
[10] GRABNER H,LEISTNER C,BISCHOF H.Semi-supervised on-line boosting for robust tracking[C]//Computer Vision:ECCV 2008:Lecture Notes in Computer Science Vol 5302.Berlin: Springer,2008:234-247.DOI:10.1007/978-3-540-88682-2_19.
[11] 贾静平,张飞舟,柴艳妹.Adaboost目标跟踪算法[J].模式识别与人工智能,2009,22(3):475-480.DOI:10.16451/j.cnki.issn1003-6059.2009.03.009.
[12] COMANICIU D,RAMESH V,MEER P.Kernel-based object tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(5):564-577.DOI:10.1109/TPAMI.2003.1195991.
[13] ROSS D A,LIM Jongwoo,LIN Rueisung,et al.Incremental learning for robust visual tracking[J].International Journal of Computer Vision,2008,77(1/2/3):125-141.DOI: 10.1007/s11263-007-0075-7.
[14] XIAO Gang,YUN Xiao,WU Jianmin.A multi-cue mean-shift target tracking approach based on fuzzified region dynamic image fusion[J].Science China Information Sciences,2012,55(3):577-589.DOI:10.1007/s11432-012-4553-3.
[15] COLLINS R T,LIU Yanxi,LEORDEANU M.Online selection of discriminative tracking features[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1631-1643.DOI:10.1109/TPAMI.2005.205.
[1] CAI Bing, ZHANG Canlong, LI Zhixin. Tracking Infrared-visible Target with Joint Histogram [J]. Journal of Guangxi Normal University(Natural Science Edition), 2017, 35(3): 37-44.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Guangxi Normal University(Natural Science Edition), All Rights Reserved.
Tel: 0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
Powered by Beijing Magtech Co. Ltd