期刊文献+

基于SURF目标跟踪算法研究 被引量:10

Target Tracking Method Based on SURF
下载PDF
导出
摘要 SIFT算法是特征图像特征提取中一种最具鲁棒性的算法,但是其在特征提取匹配上速度较慢,很难满足实时目标跟踪的要求。使用SURF特征提取方法既保持了SIFT算法的高精度的优点,又克服了速度慢的缺陷。提出使用SURF提取并且匹配目标的特征点,用重心算法计算目标的脱靶量,通过小区域跟踪方法和高速硬件平台实现目标的实时跟踪。实验证明,算法对目标的轻微旋转、部分遮挡、亮度变化具有很强的鲁棒性,跟踪速度比SIFT算法也极大提高。 SIFT is a kind of the most robust algorithm in image feature extracting,but feature extracting and matching speed is slow which can't meet the requirement of real time target tracking.The proposed method uses SURF feature extracting method which has the advantage of high precision of SIFT and overcome the disadvantage of low speed.First,it uses SURF algorithm to extract target feature and match them in following frames.Then,the bary method is used to compute the target displacement.The proposed method is programmed in high speed hardware system and using small zone tracking method in order to meet the real time requirement.Experimental results show that this method has strong robustness to little rotated, shielded,illumination changed of the target and decrease largely the computing time compared with SIFT.
出处 《长春理工大学学报(自然科学版)》 2011年第2期138-141,153,共5页 Journal of Changchun University of Science and Technology(Natural Science Edition)
基金 国家自然科学基金资助(60873163)
关键词 目标跟踪 SURF算法 特征提取 SIFT target tracking SURF feature extraction SIFT
  • 相关文献

参考文献11

  • 1贾云得.机器视觉[M].北京:科学出版社,2002..
  • 2Vasileios K, Christophoros Nikou, Aristidis Likas. Visual Tracking by Adaptive Kalman Filtering and Mean Shift [ J ]. Springer-Verlag Berlin Heidelberg, 2010,6040: 153 - 162.
  • 3Ta, D N, Chen, W C, Gelfand,N, Pulli, K.SURFTrac: Ef- ficient Tracking and Continuous Object Recognition using Local Feature Descriptors[ C ].Computer Vision and Pat- tern Recognition( CVPR'09 ), 2009: 2937-2943.
  • 4Huiyu Zhou, Yuan Yuan, Chunmei Shi. Object tracking using SIFT features and mean shift[ J ]. Computer Vision and Image Understanding, 2009, 113 : 345- 352.
  • 5张锐娟,张建奇,杨翠.基于SURF的图像配准方法研究[J].红外与激光工程,2009,38(1):160-165. 被引量:117
  • 6Bay H, Tuytelars T, Van Gool L. Speeded-Up Robust Fea- tures ( SURF )~ J ]. Computer Vision and Image Understan- ding,2008( 110): 346-359.
  • 7Khashman A, Dimililer K.Image compression using neu- ral networks and Haar waveletE J ].WSEAS Transactions on Signal Processing, 2008,5( 4 ): 330-339.
  • 8Freund Y. Boosting a weak learning algorithm by majority [ J ]. Information and Computat0n 1995( 121 ): 226-230.
  • 9Mikolajczyk K, Schmid C. An atTme invariant interest point detector[ J ]. Computer Vision-ECCV,2002 (2350): 128-142.
  • 10Lowe D. Distinctive image features from scale invariant ke-ypoints[ J ].In journal of Computer Vision, 2004,60 (2):91-110.

二级参考文献20

  • 1牛力丕,毛士艺,陈炜.基于Hausdorff距离的图像配准研究[J].电子与信息学报,2007,29(1):35-38. 被引量:21
  • 2王向军,王研,李智.基于特征角点的目标跟踪和快速识别算法研究[J].光学学报,2007,27(2):360-364. 被引量:48
  • 3李寒,牛纪桢,郭禾.基于特征点的全自动无缝图像拼接方法[J].计算机工程与设计,2007,28(9):2083-2085. 被引量:52
  • 4ZITOVA B, FLUSSER J. Image registration methods:a survey [J].Image and Vision Computing ,2003,21:977-1000.
  • 5HARRIS C G, STEPHENS M J. A combined comer and edge detector [C]//Processings Fourth Alvey Vision Conference, Manchester, 1988:147-151.
  • 6SMITH S M, BRADY J M. SUSAN-a new approach to low level image processing[J]. International Journal of Computer Vision, 1997,23(1): 45-78.
  • 7LOWE D G.Object recognition from local scale-invariant features [C]// International Conferenceon Computer Vision, Corfu, Greece Sept, 1999 : 1150-1157.
  • 8MIKOLAJCZYK K, SCHMID C. Scale & affine invariant interest point detectors[J].International Journal of Computer Vision, 2004,60(1):63-86.
  • 9LOWED G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004.60(2), 91-110.
  • 10BICEGO M , LAGORIO A, GROSSO E,et al. On the use of SIFT features for face authentication [C]//2006 IEEE Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop,2006:1-7.

共引文献147

同被引文献72

引证文献10

二级引证文献30

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