高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

多示例深度学习目标跟踪

程帅 孙俊喜 曹永刚 刘广文 韩广良

程帅, 孙俊喜, 曹永刚, 刘广文, 韩广良. 多示例深度学习目标跟踪[J]. 电子与信息学报, 2015, 37(12): 2906-2912. doi: 10.11999/JEIT150319
引用本文: 程帅, 孙俊喜, 曹永刚, 刘广文, 韩广良. 多示例深度学习目标跟踪[J]. 电子与信息学报, 2015, 37(12): 2906-2912. doi: 10.11999/JEIT150319
Cheng Shuai, Sun Jun-xi, Cao Yong-gang, Liu Guang-wen, Hang Guang-liang. Target Tracking Based on Multiple Instance Deep Learning[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2906-2912. doi: 10.11999/JEIT150319
Citation: Cheng Shuai, Sun Jun-xi, Cao Yong-gang, Liu Guang-wen, Hang Guang-liang. Target Tracking Based on Multiple Instance Deep Learning[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2906-2912. doi: 10.11999/JEIT150319

多示例深度学习目标跟踪

doi: 10.11999/JEIT150319
基金项目: 

国家自然科学基金(61172111)和吉林省科技厅项目(20090512, 20100312)

Target Tracking Based on Multiple Instance Deep Learning

Funds: 

The National Natural Science Foundation of China (61172111)

  • 摘要: 为解决多示例跟踪算法中外观模型和运动模型不足导致跟踪精度不高的问题,该文提出多示例深度学习目标跟踪算法。针对原始多示例跟踪算法中采用Haar-like特征不能有效表达图像信息的缺点,利用深度去噪自编码器提取示例图像的有效特征,实现图像信息的本质表达,易于分类器正确分类,提高跟踪精度。针对多示例学习跟踪算法中选取弱特征向量不能更换,难以反映目标自身和外界条件变化的缺点,在选择弱分类器过程中,实时替换判别力最弱的特征以适应目标外观的变化。针对原始多示例跟踪算法中运动模型中仅假设帧间物体运动不会超过某个范围,不能有效反映目标的运动状态的缺点,引入粒子滤波算法对目标进行预测,提高跟踪的准确性。在复杂环境下不同图片序列实验结果表明,与多示例跟踪算法及其他跟踪算法相比,该文算法具有更高跟踪精确度和更好的鲁棒性。
  • Wu Y, Lim J, and Yang M H. Online object tracking: a benchmark[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2411-2418.
    Ross D A, Lim J, Lin R S, et al.. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(3): 125-141.
    Zhang K, Zhang L, and Yang M H. Fast compressive tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(10): 2002-2015.
    Zhong W, Lu H C, and Yang M H. Robust object tracking via sparse collaborative appearance model[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2356-2368.
    陈思, 苏松志, 李绍滋, 等. 基于在线半监督boosting的协同训练目标跟踪算法[J]. 电子与信息学报, 2014, 36(4): 888-895.
    Chen S, Su S Z, Li S Z, et al.. A novel co-training object tracking algorithm based on online semi-supervised boosting[J]. Journal of Electronics Information Technology, 2014, 36(4): 888-895.
    Zhang K, Zhang L, Liu Q, et al.. Fast tracking via dense spatio-temporal context learning[C]. Proceedings of European Conference on Computer Vision, Zurich, Switzerland, 2014: 127-141.
    Kalal Z, Mikolajczyk K, and Matas J. Tracking- learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.
    Babenko B, Yang M H, and Belongie S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632.
    Zhang K H and Song H H. Real-time visual tracking via online weighted multiple instance learning[J]. Pattern Recognition, 2013, 46(1): 397-411.
    陈东成, 朱明, 高文, 等. 在线加权多示例学习实时目标跟踪[J]. 光学 精密工程, 2014, 22(6): 1661-1667.
    Chen D C, Zhu M, Gao W, et al.. Real-time object tracking via online weighted multiple instance learning[J]. Optics and Precision Engineer, 2014, 22(6): 1661-1667.
    宁纪锋, 赵耀博, 石武祯. 多通道Haar-like特征多示例学习目标跟踪[J]. 中国图象图形学报, 2014, 19(7): 1038-1045.
    Ning J F, Zhao Y B, and Shi W Z. Multiple instance learning based object tracking with multi-channel haar-like feature[J]. Journal of Image and Graphics, 2014, 19(7): 1038-1045.
    郑胤, 陈权崎, 章毓晋. 深度学习及其在目标和行为识别中的新进展[J]. 中国图像图形学报, 2014, 19(2): 175-184.
    Zheng Y, Chen Q, and Zhang Y. Deep learning and its new progress in object and behavior recognition[J]. Journal of Image and Graphics, 2014, 19(2): 175-184.
    Vincent P, Larochellel H, Lajoie I, et al.. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11: 3371-3408.
    程帅, 曹永刚, 孙俊喜, 等. 基于增强群跟踪器和深度学习的目标跟踪[J]. 电子与信息学报, 2015, 37(7): 1646-1653.
    Cheng S, Cao Y G, Sun J X, et al.. Target tracking based on enhanced flock of tracker and deep learning[J]. Journal of Electronics Information Technology, 2015, 37(7): 1646-1653.
    Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
    Rob H and Alan F. Discriminatively trained particle filters for complex multi-object tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 240-247.
    李天成, 孙树栋. 采用双重采样的移动机器人Monte Carlo定位方法[J]. 自动化学报, 2010, 36(9): 1279-1286.
    Li T C and Sun S D. Double-resampling based monte carlo localization for mobile robot[J]. Acta Automatica Sinica, 2010, 36(9): 1279-1286.
    Torralba A, Fergus R, and Freeman W T. 80 million tiny images: a large data set for nonparametric object and scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11): 1958-1970.
    Olshausen B and Field D. Sparse coding with an overcomplete basis set: a strategy employed by V1[J]. Vision Research, 1997, 37(23): 3311-3326.
    Dinh T B, Vo N, and Medion G. Context tracker: exploring supporters and distracters in unconstrained environments[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2011: 1177-1184.
    Zhang T, Ghanem B, Liu S, et al.. Robust visual tracking via multi-task sparse learning[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 2042-2049.
  • 加载中
计量
  • 文章访问数:  2306
  • HTML全文浏览量:  176
  • PDF下载量:  1781
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-03-17
  • 修回日期:  2015-07-27
  • 刊出日期:  2015-12-19

目录

    /

    返回文章
    返回