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

单目视觉下目标三维行为的时间尺度不变建模及识别

王蒙 戴亚平 王庆林

王蒙, 戴亚平, 王庆林. 单目视觉下目标三维行为的时间尺度不变建模及识别. 自动化学报, 2014, 40(8): 1644-1653. doi: 10.3724/SP.J.1004.2014.01644
引用本文: 王蒙, 戴亚平, 王庆林. 单目视觉下目标三维行为的时间尺度不变建模及识别. 自动化学报, 2014, 40(8): 1644-1653. doi: 10.3724/SP.J.1004.2014.01644
WANG Meng, DAI Ya-Ping, WANG Qing-Lin. Time-scale Invariant Modeling and Classifying for Object Behaviors in 3D Space Based on Monocular Vision. ACTA AUTOMATICA SINICA, 2014, 40(8): 1644-1653. doi: 10.3724/SP.J.1004.2014.01644
Citation: WANG Meng, DAI Ya-Ping, WANG Qing-Lin. Time-scale Invariant Modeling and Classifying for Object Behaviors in 3D Space Based on Monocular Vision. ACTA AUTOMATICA SINICA, 2014, 40(8): 1644-1653. doi: 10.3724/SP.J.1004.2014.01644

单目视觉下目标三维行为的时间尺度不变建模及识别

doi: 10.3724/SP.J.1004.2014.01644
基金项目: 

本文责任编委周杰

详细信息
    作者简介:

    戴亚平 北京理工大学自动化学院教授.主要研究方向为机动目标跟踪,基于网络的远程控制,多传感器数据融合.E-mail:daiyaping.bit@gmail.com

    通讯作者:

    王蒙 北京理工大学自动化学院博士研究生. 主要研究方向为计算机视觉,模式识别及信息融合.Email:vicong68@gmail.com

Time-scale Invariant Modeling and Classifying for Object Behaviors in 3D Space Based on Monocular Vision

  • 摘要: 提出一种单目视觉下在线识别目标三维行为的方法. 该方法用匹配的标记点估计帧间相似变换,然后转换相似矩阵到对数空间以获取一致的四自由度运动参数序列. 为解决持续时间敏感问题,提出基于多边形近似算法的时间尺度不变特征,并用动态规划实现特征序列的在线提取. 在行为识别阶段,基于动态时间规整训练有限类别行为模板用于匹配测试行为序列. 实验结果表明,该行为模板较对比方法类别可分性平均提高60%以上,并且可用于在线识别连续视频中的未知行为.
  • [1] Gu Jun-Xia, Ding Xiao-Qing, Wang Sheng-Jin. A survey of activity analysis algorithms. Journal of Image and Graphics, 2009, 14(3): 377-387(谷军霞, 丁晓青, 王生进. 行为分析算法综述. 中国图象图形学报, 2009, 14(3): 377-387)
    [2] [2] Ji X F, Liu H H. Advances in view-invariant human motion analysis: a review. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2010, 40(1): 13-24
    [3] [3] Parameswaran V, Chellappa R. View invariance for human action recognition. International Journal of Computer Vision, 2006, 66(1): 83-101
    [4] [4] Weinland D, Ronfard R, Boyer E. Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding, 2006, 104(2-3): 249-257
    [5] [5] Yamato J, Ohya J, Ishii K. Recognizing human action in time-sequential images using hidden Markov model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Champaign, IL: IEEE, 1992. 379-385
    [6] [6] Brand M, Oliver N, Pentland A. Coupled hidden Markov models for complex action recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Juan: IEEE, 1997. 994-999
    [7] [7] Galata A, Johnson N, Hogg D. Learning variable-length Markov models of behavior. Computer Vision and Image Understanding, 2001, 81(3): 398-413
    [8] [8] Luo Y, Wu T D, Hwang J N. Object-based analysis and interpretation of human motion in sports video sequences by dynamic Bayesian networks. Computer Vision and image Understanding, 2003, 92(2-3): 196-216
    [9] Du You-Tian, Chen Feng, Xu Wen-Li. Approach to human activity multi-scale analysis and recognition based on multi-layer dynamic bayesian network. Acta Automatica Sinica, 2009, 35(3): 225-232(杜友田, 陈峰, 徐文立. 基于多层动态贝叶斯网络的人的行为多尺度分析及识别方法.
    [10] 自动化学报, 2009, 35(3): 225-232)
    [11] Bobick A F, Davis J W. The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(3): 257-267
    [12] Yilmaz A, Shah M. Actions sketch: a novel action representation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005. 984-989
    [13] Webb J A, Aggarwal J K. Structure from motion of rigid and jointed objects. Artificial Intelligence, 1982, 19(1): 107-130
    [14] Perez J C, Vidal E. Optimum polygonal approximation of digitized curves. Pattern Recognition Letters, 1994, 15(8): 743-750
    [15] Gavrila D M, Davis L S. 3-D model-based tracking of humans in action: a multi-view approach. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE, 1996. 73-80
    [16] Bar-Shalom Y. Tracking and Data Association. San Diego, CA: Academic Press Professional, Inc., 1987
    [17] Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188
    [18] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. International Journal of Computer Vision, 1988, 1(4): 321-331
    [19] Kehl R, Bray M, Van Gool L. Full body tracking from multiple views using stochastic sampling. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA: IEEE, 2005. 129-136
    [20] Johansson G. Visual motion perception. Scientific American, 1975, 232(6): 76-88
    [21] Gritai A, Sheikh Y, Shah M. On the use of anthropometry in the invariant analysis of human actions. In: Proceedings of the 17th International Conference on Pattern Recognition. Washington, DC: IEEE Computer Society, 2004. 923-926
    [22] Gu Jun-Xia, Ding Xiao-Qing, Wang Sheng-Jin. Human 3D model-based 2D action recognition. Acta Automatica Sinica, 2010, 36(1): 46-53(谷军霞, 丁晓青, 王生进. 基于人体行为3D模型的2D行为识别. 自动化学报, 2010, 36(1): 46-53)
    [23] Bhuyan M K. FSM-based recognition of dynamic hand gestures via gesture summarization using key video object planes. International Journal of Computer and Communication Engineering, 2012, 1(6): 248-259
    [24] Lv F, Nevatia R. Single view human action recognition using key pose matching and viterbi path searching. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN: IEEE, 2007. 1-8
    [25] Tuytelaars T, Mikolajczyk K. Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, 2008, 3(3): 177-280
    [26] Rosten E, Drummond T. Fusing points and lines for high performance tracking. In: Proceedings of the IEEE International Conference on Computer Vision. Beijing: IEEE, 2005. 1508-1515
    [27] Zitov
    [28] B, Flusser J. Image registration methods: a survey. Image and Vision Computing, 2003, 21(11): 977-1000
    [29] Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
    [30] Rosten E, Porter R, Drummond T. Faster and better: a machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(1): 105-119
    [31] Dunham J G. Optimum uniform piecewise linear approximation of planar curves. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(1): 67-75
  • 加载中
计量
  • 文章访问数:  1923
  • HTML全文浏览量:  64
  • PDF下载量:  1100
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-07-18
  • 修回日期:  2013-12-16
  • 刊出日期:  2014-08-20

目录

    /

    返回文章
    返回