2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

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

视频中旋转与尺度不变的人体分割方法

薄一航 HAOJiang

薄一航, HAOJiang. 视频中旋转与尺度不变的人体分割方法. 自动化学报, 2017, 43(10): 1799-1809. doi: 10.16383/j.aas.2017.c150841
引用本文: 薄一航, HAOJiang. 视频中旋转与尺度不变的人体分割方法. 自动化学报, 2017, 43(10): 1799-1809. doi: 10.16383/j.aas.2017.c150841
BO Yi-Hang, HAO Jiang. A Rotation-and Scale-invariant Human Parts Segmentation in Videos. ACTA AUTOMATICA SINICA, 2017, 43(10): 1799-1809. doi: 10.16383/j.aas.2017.c150841
Citation: BO Yi-Hang, HAO Jiang. A Rotation-and Scale-invariant Human Parts Segmentation in Videos. ACTA AUTOMATICA SINICA, 2017, 43(10): 1799-1809. doi: 10.16383/j.aas.2017.c150841

视频中旋转与尺度不变的人体分割方法

doi: 10.16383/j.aas.2017.c150841
基金项目: 

北京市教委科研计划一般项目-目标跟踪与分割算法在电影抠像中的应用与研究 KM201710050001

详细信息
    作者简介:

    HAOJiang:HAO Jiang 波士顿学院计算机科学系副教授.主要研究方向为图像匹配, 目标检测, 目标跟踪, 姿势和行为估计.E-mail:hjiang@cs.bc.edu

    通讯作者:

    薄一航 北京电影学院美术学院讲师.2011年博士毕业于北京交通大学, 2011~2014年分别在中国科学院自动化所与波士顿学院从事博士后研究工作.主要研究方向为图像与视频分割, 人的行为和姿势估计, 目标跟踪, 交互设计.本文通信作者.E-mail:boyihang@sina.com

A Rotation-and Scale-invariant Human Parts Segmentation in Videos

Funds: 

Beijing Municipal Education Commission, General Plan of Scientific Research Plan-Application and Research on Object Tracking and Segmentation in Film Keying KM201710050001

More Information
    Author Bio:

    Associate professor in the Computer Science Department, Boston College, USA. His research interest covers image matching, object detection, tracking, pose and action recognition

    Corresponding author: BO Yi-Hang  Assistant professor in Fine Art Department, Beijing Film Academy. She received her Ph. D. degree at the School of Computer and Information Technology, Beijing Jiaotong University in 2011, postdoctor at the Institute of Automation, Chinese Academy of Sciences, China and Boston College, USA from 2011 to 2014. Her research interest covers image and video segmentation, human pose and action recognition, object tracking and interactive design. Corresponding author of this paper.E-mail:boyihang@sina.com
  • 摘要: 提出了一种旋转与尺度不变的人身体部位所在区域的视频分割方法.方法中不仅考虑到躯干与四肢之间的关系,还考虑到四肢之间的相互关系,通过空间与时间的连续性约束对每帧中各个可能的身体部位进行优化组合,并巧妙地用动态规划对非线性图模型进行优化,且不受运动目标尺度变化与各种翻转运动的影响.该方法首先用动态规划的优化方法得到每一帧中最优的N个身体部位组合,将每一个组合作为图模型中的一个节点,并用动态规划对所有帧中的各个组合所构成的网格状图结构进行优化,最终得到每一帧中最优的身体部位组合.实验结果表明,该视频分割方法不仅适用于行人视频,还适用于具有各种姿势的运动视频,且具有较好的鲁棒性.
    1)  本文责任编委 桑农
  • 图  1  “图案结构”检测结果与本方法分割结果图

    Fig.  1  Detection result of "pictorial structure" method and the segmentation result of proposed method

    图  2  旋转与尺度不变的视频分割方法鸟瞰图

    Fig.  2  The bird-view of rotation and scale invariant video segmentation method

    图  3  单帧内与相邻帧之间身体部位关系图

    Fig.  3  Human body parts relationships in single frame and between adjacent frames

    图  4  身体部位关系解析图

    Fig.  4  The relationship of human body parts

    图  5  去背景后效果图

    Fig.  5  Results after background removed

    图  6  本方法在5段测试视频上的部分分割结果

    Fig.  6  Sample results of proposed methods on five test videos

    图  7  文献[31]的方法与本方法测试结果对比示例

    Fig.  7  Example results of the method in [31] and proposed method

    图  8  nbest方法检测结果与本方法结果示例

    Fig.  8  Example results of nbest method and proposed method

    图  9  该方法与nbest方法实验结果的正确率曲线图

    Fig.  9  Detection rate comparisons of nbest and proposed method

    图  10  行人姿势估计结果

    Fig.  10  Pedestrian pose estimation results

    表  1  该方法和nbest方法分别与GT的比较结果

    Table  1  Comparison of proposed method and GT, nbest method and GT

    nbest Ours nbest Ours nbest Ours nbest Ours nbest Ours
    Arms Arms Legs Legs Torso Torso All All Mean Mean
    Video 1 13.96 % 25.90 % 45.30 % 37.37 % 24.99 % 40.31 % 45.70 % 62.45 % 32.49 % 41.51 %
    Video 2 12.15 % 32.49 % 24.71 % 43.87 % 42.61 % 56.41 % 38.47 % 62.43 % 29.49 % 48.80 %
    Video 3 12.62 % 25.00 % 42.69 % 42.99 % 45.41 % 44.03 % 48.75 % 67.98 % 37.37 % 45.00 %
    Video 4 22.54 % 25.93 % 44.76 % 54.29 % 51.20 % 53.81 % 50.21 % 67.77 % 42.18 % 50.45 %
    Video 5 22.29 % 56.10 % 65.32 % 64.17 % 49.75 % 63.18 % 62.96 % 84.58 % 50.08 % 67.01 %
    Mean 16.71 % 33.08 % 44.56 % 48.54 % 42.79 % 51.55 % 49.22 % 69.04 % 38.32 % 50.55 %
    下载: 导出CSV
  • [1] Criminisi A, Cross G, Blake A, Kolmogorov V. Bilayer segmentation of live video. In:Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. New York, USA:IEEE, 2006. 53-60
    [2] Cheung S C S, Kamath C. Robust techniques for background subtraction in urban traffic video. In:Proceedings of SPIE 5308, Visual Communications and Image Processing. San Jose, USA:SPIE, 2004, 5308:881-892
    [3] Hayman E, Eklundh J. Statistical background subtraction for a mobile observer. In:Proceedings of the 9th IEEE International Conference on Computer Vision. Nice, France:IEEE, 2003. 67-74
    [4] Ren Y, Chua C S, Ho Y K. Statistical background modeling for non-stationary camera. Pattern Recognition Letters, 2003, 24(1-3):183-196 doi: 10.1016/S0167-8655(02)00210-6
    [5] Giordano D, Murabito F, Palazzo S, Spampinato C. Superpixel-based video object segmentation using perceptual organization and location prior. In:Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA:IEEE, 2015. 4814-4822
    [6] Brendel W, Todorovic S. Video object segmentation by tracking regions. In:Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan:IEEE, 2009. 833-840
    [7] Li F X, Kim T, Humayun A, Tsai D, Rehg J M. Video segmentation by tracking many figure-ground segments. In:Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, Australia:IEEE, 2013. 2192-2199
    [8] Varas D, Marques F. Region-based particle filter for video object segmentation. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA:IEEE, 2014. 3470-3477
    [9] Arbeláez P A, Pont-Tuset J, Barron J T, Marques F, Malik J. Multiscale combinatorial grouping. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA:IEEE, 2014. 328-335
    [10] Tsai Y H, Yang M H, Black M J. Video segmentation via object flow. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA:IEEE, 2016.
    [11] Ramakanth S A, Babu R V. Seamseg:video object segmentation using patch seams. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA:IEEE, 2014. 376-383
    [12] Faktor A, Irani M. Video segmentation by non-local consensus voting. In:Proceedings British Machine Vision Conference 2014. Nottingham:BMVA Press, 2014.
    [13] Papazoglou A, Ferrari V. Fast object segmentation in unconstrained video. In:Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, Australia:IEEE, 2013. 1777-1784
    [14] Rother C, Kolmogorov V, Blake A. "Grabcut":interactive foreground extraction using iterated graph cuts. Acm Transactions on Graphics, 2004, 23(3):309-314 doi: 10.1145/1015706
    [15] Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA:IEEE, 2014. 580-587
    [16] Lin T Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick C L. Microsoft COCO:common objects in context. In:Proceedings of the 13th European Conference. Zurich, Switzerland:Springer International Publishing, 2014. 740-755
    [17] Endres I, Hoiem D. Category-independent object proposals with diverse ranking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(2):222-234 doi: 10.1109/TPAMI.2013.122
    [18] Krähenbühl P, Koltun V. Geodesic object proposals. In:Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland:Springer International Publishing, 2014. 725-739
    [19] Zhang D, Javed O, Shah M. Video object segmentation through spatially accurate and temporally dense extraction of primary object regions. In:Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, Oregon, USA:IEEE, 2013. 628-635
    [20] Fragkiadaki K, Arbelaez P, Felsen P, Malik J. Learning to segment moving objects in videos. In:Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA:IEEE, 2015. 4083-4090
    [21] Perazzi F, Wang O, Gross M, Sorkine-Hornung A. Fully connected object proposals for video segmentation. In:Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015. 3227-3234
    [22] Kundu A, Vineet V, Koltun V. Feature space optimization for semantic video segmentation. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, Nevada, USA:IEEE, 2016.
    [23] Seguin G, Bojanowski P, Lajugie R, Laptev I. Instance-level video segmentation from object tracks. In:Proceeding of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, Nevada, USA:IEEE, 2016.
    [24] Lee Y J, Kim J, Grauman J. Key-Segments for video object segmentation. In:Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spanish:IEEE, 2011. 1995-2002
    [25] Tsai D, Flagg M, Rehg J. Motion coherent tracking with multi-label MRF optimization. In:Proceedings of the British Machine Vision Conference 2010. Aberystwyth:BMVA Press, 2010. 190-202
    [26] Ramanan D, Forsyth D A, Zisserman A. Tracking people by learning their appearance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1):65-81 doi: 10.1109/TPAMI.2007.250600
    [27] Yang Y, Ramanan D. Articulated pose estimation with flexible mixtures-of-parts. In:Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA:IEEE, 2011. 1385-1392
    [28] Endres I, Hoiem D. Category independent object proposals. In:Proceedings of the 11th European Conference on Computer Vision. Heraklion, Crete, Greece:Springer, 2010. 575-588
    [29] Ling H B, Jacobs D W. Shape classification using the innerdistance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2):286-299 doi: 10.1109/TPAMI.2007.41
    [30] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005. 886-893
    [31] Park D, Ramanan D. N-best maximal decoders for part models. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 2627-2634
    [32] Sigal L, Black M J. HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion. Techniacl Report CS-06-08. Brown University, USA, 2006
    [33] Grundmann M, Kwatra V, Han M, Essa I. Efficient hierarchical graph based video segmentation. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 2141-2148
    [34] Oneata D, Revaud J, Verbeek J, Schmid C. Spatio-temporal object detection proposals. In: Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland: Springer International Publishing, 2014. 737-752
    [35] Pele O, Werman M. Fast and robust earth mover0s distance. In: Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009. 460-467
    [36] Brox T, Malik J. Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(3): 500-513 doi: 10.1109/TPAMI.2010.143
  • 加载中
图(10) / 表(1)
计量
  • 文章访问数:  1567
  • HTML全文浏览量:  264
  • PDF下载量:  503
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-12-14
  • 录用日期:  2016-10-26
  • 刊出日期:  2017-10-20

目录

    /

    返回文章
    返回