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结合目标预测位置的压缩跟踪

DOI: 10.11834/jig.20140608

Keywords: 目标跟踪,投影矩阵,压缩跟踪,参数自适应

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Abstract:

目的提出结合目标预测位置的压缩跟踪算法用于提高目标跟踪的准确度。方法选择随机间距稀疏Toeplitz矩阵作为投影矩阵,对原始多尺度Haar-like特征进行压缩;然后,将样本与MeanShift算法框架下的预测位置的距离权重输入Bayes分类器,形成分类背景与目标的判别函数;最后对参数的更新方式进行优化,提出了参数自适应的学习模式。结果与目前较流行的6种目标跟踪算法在20个具有挑战性的序列中进行比较,实验结果表明本文提出的算法平均跟踪成功率比压缩跟踪算法将近高27%,平均运行时间为0.15s/帧。结论本文采用结合预测位置的压缩跟踪算法,在参数更新阶段采用了非线性参数学习模式,实验结果表明结合目标预测位置的跟踪算法比一般的跟踪算法更具有鲁棒性,更能适应遮挡等情况,跟踪的效果也更加平滑。

References

[1]  Hare S, Saffari A, Torr P H S. Struck: structured output tracking with kernels[C]//Proceedings of IEEE International Conference on Computer Vision. Barcelona:IEEE, 2011:263-270.
[2]  Zhang K H, Zhang L, Yang M H. Real-time compressive tracking[C]//Proceedings of the 12th European Conference on Computer Vision. Florence, Italy:IEEE, 2012:864-877.
[3]  Mei X, Ling H B. Robust visual tracking and vehicle classification via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,33(11):2259-2272.
[4]  Babenko B, Yang M H, Belongie S. Visual tracking with online multiple instance learning[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL:IEEE, 2009:983-990.
[5]  Shao W Z, Wei Z H. Advances and perspectives on compressed sensing theory[J]. Journal of Image and Graphics, 2012, 17(1): 1-12. [邵文泽, 韦志辉. 压缩感知基本理论:回顾与展望[J].中国图象图形学报, 2012, 17(1): 1-12]
[6]  Yuan G L, Xue M G, Xie K, et al. Mean shift tracking with multiple color histograms adaptive integration[J]. Journal of Ima-ge and Graphics, 2011, 16(10):1832-1840.[袁广林, 薛模根, 谢恺, 等. 多颜色直方图自适应组合Mean Shift跟踪[J]. 中国图象图形学报, 2011, 16(10):1832-1840.]
[7]  Jeyakar J, Babu R V, Ramakrishnan K R. Robust object tracking with background-weighted local kernels[J]. Computer Vision and Image Understanding, 2008, 112(3):296-309.
[8]  Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25 (5):564-577.
[9]  Ning J, Zhang L, Zhang D, et al. Robust mean shift tracking with corrected background-weighted histogram[J]. Computer Vision, 2012, 6(1):62-69.
[10]  Zhang C, Yang H R, Wei S. Compressive sensing based on deterministic sparse toeplitz measurement matrices with random pitch[J]. Acta Automatica Sinica, 2012, 38(8): 1362-1369.[张成, 杨海蓉, 韦穗. 基于随机间距稀疏Toeplitz测量矩阵的压缩传感[J]. 自动化学报. 2012, 38(8): 1362-1369.]
[11]  Diaconis P, Freedman D. Asymptotics of graphical projection pursuit[J]. The Annals of Statistics, 1984,12(3):228-235.
[12]  Zhang W, Mu Z C, Yuan L. Fast ear detection and tracking based on improved AdaBoost algorithm[J]. Journal of Image and Graphics, 2007, 12(2):222-227.[张惟, 穆志纯, 袁立. 基于改进AdaBoost算法的人耳检测与跟踪[J]. 中国图象图形学报, 2007, 12(2):222-227.]
[13]  Jain A, Nandakumar K, Ross A. Score normalization in multimodal biometric systems[J]. Pattern Recognition, 2005, 38(12):2270-2285.
[14]  Cappelli R, Maio D, Maltoni D. Combining fingerprint classifiers[C]//Proceedings of the First International Workshop on Multiple Classifier Systems. Cagliari, Italy: Springer, 2000:351-361.
[15]  Kalal Z, Mikolajczyk K, Matas J. Face-TLD: tracking-learning-detection applied to faces[C]//Proceedings of IEEE International Conference on Image Processing. Hong Kong, China: IEEE, 2010:3789-3792.
[16]  Li H X, Shen C H, Shi Q F. Real-time visual tracking using compressive sensing[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI:IEEE, 2011: 1305-1312.
[17]  Santner J, Leistner C, Saffari A, et al. PROST: parallel robust online simple tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA:IEEE, 2010:723-730.
[18]  Cehovin L, Kristan M, Leonardis A. Robust visual tracking using an adaptive coupled-layer visual model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(4):941-953.
[19]  Junseok K, Kyoung M L. Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping monte carlo sampling[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL:IEEE, 2009: 1208-1215.
[20]  Godec M, Roth P M, Bischof H. Hough-based tracking of non-rigid objects[C]//Proceedings of IEEE International Conference on Computer Vision. Barcelona:IEEE, 2011: 81-88.
[21]  更多...
[22]  Junseok K, Kyoung M L. Visual tracking decomposition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA:IEEE, 2010:1269-1276.

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