Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 181-186.doi: 10.11896/JsJkx.190500093

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Sparse Representation Target Tracking Algorithm Based on Multi-scale Adaptive Weight

CHENG Zhong-Jian, ZHOU Shuang-e and LI Kang   

  1. School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China
  • Published:2020-07-07
  • About author:CHENG Zhong-Jian.His main research interests include data mining and so on.
    ZHOU Shuang-e, professor, is a senior member of China Computer Federation.Her main research interests include big data analysis technology, computer application technology, information secu-rity.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61370002).

Abstract: Target tracking is an important research field in computer vision.It is widely used in many aspects such as traffic navigation,autonomous driving and robotics.The generative model algorithm ASLA based on local sparse representation is fast and has high tracking accuracy,but it often loses its target in the face of complex tracking environment,such as target partial occlusion and dramatic change of target appearance.This paper analyzes the tracking principle of the original algorithm to get the cause of target tracking loss.Based on the ASLA algorithm,a three-point improvement method is proposed.1) Adaptive tracking of the target area size using multi-scale blocking method to obtain complementary target local information.2) In the feature pooling process of ASLA,block adaptive weight is modeled based on block reconstruction error to distinguish the discrimination information contained in different blocks,and introducing target occlusion information at different scales as weights in multi-scale pooling features.3)When the template is updated,the weight of the latest tracking results in subspace sparse representation is enhanced to make the updated template more similar to the recent tracking results,and improve the robustness of the algorithm.Experimental results show that the algorithm has higher tracking accuracy than algorithms such as ASLA in complex tracking environment,and can track the target in real time and accurately.

Key words: Target tracking, Multi-scale adaptive weight, Sparse representation, ASLA, Generative

CLC Number: 

  • TP391.41
[1] ZHANG M,WANG J.An Interactive Likelihood Target Trac-king Algorithm Based on Deep Learning.Computer Science,2019,46(2):279-285.
[2] DANELLJAN M,SHAHBAZ KHAN F,FELSBERG M,et al.Adaptive color attributes for real-time visual tracking//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:1090-1097.
[3] ZHANG T,JIA K,XU C,et al.Partial occlusion handling for visual tracking via robust part matching//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:1258-1265.
[4] WANG Y X,ZHAO Q J,CAI Y M,et al.Target tracking based on self-reconstructed particle filter algorithm.Chinese Journal of Computers,2016,39(7):1294-1306.
[5] JIANG W T,LIU W J,YUAN W,et al.Visual Quantum Target Tracking Method.Journal of Software,2016,27(11):2961-2984.
[6] GRABNER H,BISCHOF H.On-line boosting and vision//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06).IEEE,2006:260-267.
[7] KALAL Z,MATAS J,MIKOLAJCZYK K.Pn learning:Bootstrapping binary classifiers by structural constraints//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.IEEE,2010:49-56.
[8] JIANG W T,LIU W J,YUAN W.Target tracking based on soft feature theory.Chinese Journal of Computers,2016,39(7):1334-1355.
[9] XUE M G,ZHU H,YUAN G L.Online Robust Discriminant Dictionary Learning Visual Tracking.Chinese Journal of Electronics,2015,44(4):838-845.
[10] GAO S,CHIA L T,TSANG I W H,et al.Concurrent single-label image classification and annotation via efficient multi-layer group sparse coding.IEEE Transactions on Multimedia,2014,16(3):762-771.
[11] HU W,LI W,ZHANG X,et al.Single and multiple obJect trac-king using a multi-feature Joint sparse representation.IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(4):816-833.
[12] JEPSON A D,FLEET D J,EL-MARAGHI T F.Robust online appearance models for visual tracking.IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1296-1311.
[13] MEI X,LING H,WU Y,et al.Minimum error bounded efficient 1 tracker with occlusion detection//CVPR 2011.IEEE,2011:1257-1264.
[14] REN X,RAMANAN D.Histograms of sparse codes for obJect detection//2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2013:3246-3253.
[15] TONG Y,FEI S Z,SHEN J.Fast Target Tracking Method Based on TLD Framework.Application Research of Compu-ters,2018,35(1):317-320.
[16] KALAL Z,MIKOLAJCZYK K,MATAS J.Tracking-learningdetection.IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7):1409-1422.
[17] JIA X,LU H,YANG M H.Visual tracking via adaptive structural local sparse appearance model//2012 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2012:1822-1829.
[18] MEI X,LING H.Robust visual tracking using 1 minimization//2009 IEEE 12th International Conference on Computer Vision.IEEE,2009:1436-1443.
[19] ZHANG K,ZHANG L,YANG M H.Real-time compressive tracking//European Conference on Computer Vision.Springer,Berlin,Heidelberg,2012:864-877.
[20] BABENKO B,YANG M H,BELONGIE S.Visual tracking with online multiple instance learning//2009 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:983-990.
[21] BAO C,WU Y,LING H,et al.Real time robust l1 tracker using accelerated proximal gradient approach//2012 IEEEConfe-rence on Computer Vision and Pattern Recognition.IEEE,2012:1830-1837.
[22] WU Y,LIM J,YANG M H.Online obJect tracking:A benchmark//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2013:2411-2418.
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