期刊文献+

利用局部特征联合匹配的非刚体目标跟踪 被引量:5

Non-rigid object tracking using joint matching of local features
下载PDF
导出
摘要 针对视频序列中非刚体目标的跟踪问题,提出了基于局部特征联合匹配的快速跟踪算法.算法将基于关键点的特征匹配问题转化为求解平衡指派的最优化问题,进而依据整体匹配最优的原则实现特征的联合匹配.跟踪过程为:首先分别提取目标模板和当前搜索区域的局部关键点并进行特征描述;然后依据联合匹配策略确定目标模板关键点在输入帧图像中的匹配结果;最后依据匹配结果确定目标在输入帧图像中的位置和尺度.实验结果表明,该算法对目标的非刚性形变具有较强的鲁棒性,能够适应复杂的背景变化并获得稳定的跟踪结果. To the problem of non-rigid obj ect tracking in video sequence,an efficient tracking algorithm based on joint matching of local features is proposed.The feature matching problem based on key points is translated into the optimization problem of balance assignment,and completed via joint matching of features under the maximization of holistic similarity.The tracking process is as follows.Firstly,local key points of the obj ect template and current searching area are detected and described using the information on their neighborhoods.Then,the key points are marched via the proposed joint matching strategy,and the right matching pairs are picked out. Finally, the obj ect’s location and size are calculated according to the matching results,and the tracking result of the current frame is outputted.Experimental results indicate that the proposed algorithm is robust to obj ect”s non-rigid deformation,and can cope with the complex background change to obtain a stable tracking result.
出处 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2014年第6期160-166,共7页 Journal of Xidian University
基金 国家自然科学基金资助项目(61175029 61203268) 陕西省自然科学基金资助项目(2011JM8015)
关键词 视觉跟踪 局部特征 联合匹配 平衡指派 非刚体目标 visual tracking local feature joint matching balance assignment non-rigid object
  • 相关文献

参考文献10

  • 1Adam A, Rivlin E, Shimshoni h Robust Fragments-based Tracking Using the Integral Histogram [C] //Proceedings of Computer Vision and Pattern Recognition. Piseataway: IEEE, 2006: 798-805.
  • 2Nejhum S M S, Ho J, Yang M H. Online Visual Tracking with Histograms and Articulating Blocks [J]. Computer Vision and Image Understanding, 2010, 114(8) : 901-914.
  • 3Kwon J, Lee K. Tracking of a Non-rigid Object via Patch-based Dynamic Appearance Modeling and Adaptive Basin Hopping Monte Carlo Sampling [C] // Proceedings of Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009: 1208-1215.
  • 4Hare S, Saffari A, Torr P H S. Efficient Online Structured Output Learning for Keypoint-based Object Tracking [C] // Proceedings of Computer Vision and Pattern Recognition. Piscatawav: IEEE, 2012: 1894-1901.
  • 5李远征,卢朝阳,李静.一种基于多特征融合的视频目标跟踪方法[J].西安电子科技大学学报,2012,39(4):1-6. 被引量:15
  • 6Zhang K, Zhang L, Yang M H. Real-time Compressive Tracking [C] // Proceedings of Computer Vision and Pattern Recognition. Heidelberg: Springer-Verlag, 2012: 864-877.
  • 7Wang D, Lu H, Yang M H. Online Object Tracking with Sparse Prototypes [J]. IEEE Transactions on Image Processing, 2013, 22(1): 314-325.
  • 8Lowe D. Distinctive Image Features from Scale-Invariant Keypoints [J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
  • 9Harris C, Stephens M. A Combined Corner and Edge Detector [C] // Proceedings of Fourth Alvey Vision Conference. Manchester: University of Manchester, 1988: 147-151.
  • 10Kuhn H. The Hungarian Method for Assignment Problem [J]. Naval Research Logistics, 2005, 2(1): 7-21.

二级参考文献14

  • 1Comaniciu D, Ramesh V, Meer P. Kernel-based Object Tracking [J] . IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577.
  • 2Zhou S K, Chellappa R, Moghaddam B. Visual Tracking and Recognition Using Appearance-adaptive Models for Particle Filters[J]. IEEE Trans on Image Processing, 2004, 13(11) : 1491-1506.
  • 3Perez P, Vermaak J, Blake A. Data Fusion for Visual Tracking with Particles[J]. Proceedings of the IEEE, 2004, 92(3) : 495-513.
  • 4Valtteri T, Pietikainen M. Multi-object Tracking Using Color, Texture and Motion[C]//Proc of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Chicago: IEEE, 2007: 1-7.
  • 5Birchfield S. Elliptical Head Tracking Using Intensity Gra-dients and Color Histograms [C]//Proc of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Santa: IEEE, 1998: 232-237.
  • 6Ido L, Michael L, Ehud R. Tracking by Affine Kernel Transformations Using Color and Boundary Cues[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(1): 164-171.
  • 7Maggio E, Smeraldi F, Cavallaro A. Adaptive Multi-feature Tracking in a Particle Filtering Framework[J]. IEEE Trans on Circuits and Systems for Video Technology, 2007, 17(10) : 1348-1359.
  • 8Wang X, Tang Z M. Modified Particle Filter-based Infrared Pedestrian Tracking[J]. Infrared Physics and Technology, 2010, 53(4): 280-287.
  • 9Oiala T, Pietikainen M, Maenpaa T. Multiresolution Grayscale and Rotation Invariant Texture Classification with Local Binary Patterns[J].IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(7) : 971-987.
  • 10Fisher R. Caviar Test Case Scenarios[DB/OL]. [2011-09-15]. http://homepaes, inf. ed. ac. uk/rbI/CAVIARDATA1/.

共引文献14

同被引文献60

  • 1贾静平,柴艳妹,赵荣椿.一种健壮的目标多自由度Mean Shift序列图像跟踪算法[J].中国图象图形学报,2006,11(5):707-713. 被引量:10
  • 2Karavasilis V, Nikou C, Likas A. Visual Tracking Using the Earth Mover's Distance Between Gaussian Mixtures and Kalman Filtering [J]. Image and Vision Computing, 2011, 29(5) : 295-305.
  • 3Leichter I. Mean Shift Trackers with Cross-Bin Metrics [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 695-706.
  • 4Wu J, Rehg J M. Beyond the Euclidean Distance: Creating Effective Visual Codebooks Using the Histogram Intersection Kernel [C] //Proceedings of 12th International Conference on Computer Vision. Piscataway: IEEE, 2009: 630-637.
  • 5Sarra S, Amel B. Fast Scalable Retrieval of Multispectral Images with Kullbaek-Leibler Divergence [C] //Proceedings of 17th IEEE International Conference on Image Processing. Piscataway: IEEE, 2010: 2333-2336.
  • 6Rubner Y, Tomasi C, and Guibas L. The Earth Mover's Distance as a Metric for Image Retrieval [J]. International Journal of Computer Vision, 2000, 40(2) : 99-121.
  • 7Ling H, Okada K. EMD-LI: An Efficient and Robust Algorithm for Comparing Histogram-Based Descriptors [C]// Lecture Notes in Computer Science: 3953. Berlin: Springer, 2006: 330-343.
  • 8Wu Y, Lim J, Yang M H. Online object tracking: a berlchmark[C]// Proc. of the Computer Vision and Pattern Recognition, 2013 : 2411 - 2418.
  • 9Comaniciu D, Ramesh V, Meer P. Real-time tracking of non rigid objects using mean shift[C]//Proc, of the Computer Vision and Pattern Recognition, 2000 : 142 - 149.
  • 10Qing W, Feng C, Wen L X, et al. Object trackirlg via partial least squares analysis[J]. IEEE Trans. on Image Processing, 2012, 21(10) :4454 - 4465.

引证文献5

二级引证文献27

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部