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基于NTSS和压缩感知的人脸跟踪算法 被引量:1

Face Tracking Algorithm Based on NTSS and Compressed Sensing
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摘要 提出基于NTSS和压缩感知的人脸跟踪算法,采用不同的搜索半径和步长双重采样,运用NTSS搜索方法作为目标的检测方法。通过国际标准的人脸数据库AFW和MALF视频序列进行算法的测试,并使用中心距离均值算法对压缩感知算法和基于NTSS压缩感知的人脸跟踪算法进行比对,得出基于NTSS和压缩感知的人脸跟踪算法最大可降低59. 01%的误差;此外NTSS压缩感知人脸跟踪算法在人脸发生遮挡再跟踪、人脸几何变换和在暗光条件下具有较好的鲁棒性。 This paper proposes a face tracking algorithm based on NTSS and compressed sensing. It uses different search radius and step double sampling, and uses NTSS search method as the target detection method. The algorithm is tested by the international standard face database AFW and MALF video sequences, and the central distance mean algorithm is used to compare the compressed sensing algorithm with the face tracking algorithm based on NTSS compressed sensing. The results are that the face tracking algorithm can reduce the error by 59.01%. In addition, the NTSS compressed sensing face tracking algorithm has better robustness in face occlusion re-tracking, face geometric transformation and under dark conditions.
作者 徐杰 郭春赫 孙超 邓湘奇 XU Jie;GUO Chunhe;SUN Chao;DENG Xiangqi(School of Electronic and Information Engineering, Heilongjiang University of Science and Technology,Harbin 150022, China)
出处 《实验室研究与探索》 CAS 北大核心 2019年第3期68-71,164,共5页 Research and Exploration In Laboratory
基金 黑龙江省自然科学基金面上项目(F200921)
关键词 目标跟踪 双重采样 NTSS搜索算法 target tracking double sampling NTSS search algorithm
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  • 1WANG S, LU H CH, YANG F, etal.. Superpix- el tracking [C]. Compute Vision (ICCV), 2011: 1323-1330.
  • 2ORON S, AHARON B H, LEVI D, et al.. Local- ly orderless tracking [C]. Computer Vision and Pattern Recognition, IEEE Computer Society Con- ference, 2012.
  • 3KWON J, LEE K M. Tracking of a non-rigid object via patch-based dynamic appearance modeling and a- daptive basin hopping Monte Carlo sampling [C]. Computer Vision and Pattern Recognition, IEEE Computer Society Conference, 2009,1208-1215.
  • 4KALAL Z, MATAS J, MIKOLAJCZYK K. On line learning of robust object detectors during unsta hie tracking [C]. Computer Visiotl Workshops ( IC CV Workshops), 2009 : 1417-1424.
  • 5GRABNER H, GRABNER M, BISCHOF H. Real time tracking via on-line boosting [C]. Proceedings of British Machine Vision Conference, 2006, 1: 47-56.
  • 6ADAM A, RIVLIN E, SHIMSHON L. Robust frag- ments-based tracking using the integral histogram [ C ]. Computer Vision and Pattern Recognition,IEEE Computer Society Conference, 2006 : 798- 805.
  • 7NEJHUM S M S, HO J, YANG M H. Visual tracking with histograms and articulating blocks [C]. Computer Vision and Pattern Recognition, IEEE Computer Society Conference, 2008 : 1-8.
  • 8YANG J CH, YU K, HUANG T. Supervised Translation-Invariant sparse coding [C]. Computer Vision and Pattern Recognition (CVPR), 2010: 3517-3524.
  • 9LI H X, SHEN CH H. Real-time visual tracking using compressive sensing [C]. Computer Vision and Pattern Recognition (CVPR), 2011 : 1305- 1312.
  • 10ZHANG K H, ZHANG L, YANG M H. Real- time compressive tracking [C]. European Confer- ence on Computer Vision, 2012.

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