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航空学报  2013 

基于改进SURF和P-KLT算法的特征点实时跟踪方法研究

DOI: 10.7527/S1000-6893.2013.0206, PP. 1204-1214

Keywords: 特征点提取,SURF算法,KLT算法,目标跟踪,Greedy,Snake算法

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Abstract:

针对视频序列中运动目标的实时跟踪问题,提出一种基于改进SURF算法和金字塔KLT算法相结合的特征点跟踪方法。首先人工标定目标区域,利用改进的SURF算法分块快速提取具有高鲁棒性、独特性的特征点;然后在后续帧中应用金字塔KLT匹配算法对特征点进行稳定跟踪,采用基于统计的方法剔除错误匹配对;最后利用GreedySnake分割算法提取轮廓确定更加精准的位置信息,更新目标区域。为使算法更具鲁棒性,还设计了离散点筛选、自适应更新策略。利用飞行视频数据库进行了大量的仿真,结果表明该算法适用于多尺度图像序列中位置、姿态发生快速变化且结构简单的飞行器的稳定跟踪。帧平均时间为31.8ms,比SIFT+P-KLT跟踪算法减少47.1%;帧几何中心、目标轮廓面积平均误差分别为5.03像素、16.3%,分别比GFTT+P-KLT跟踪算法减少27.2%、56.9%,比SIFT跟踪算法减少38.6%、68.4%。

References

[1]  Zhou H Y, Yuan Y, Zhang Y, et al. Non-rigid object tracking in complex scenes. Pattern Recognition Letters, 2009, 30(2): 98-102.
[2]  Sivic J, Schaffalitzky F, Zisserman A. Object level grouping for video shots. The 8th European Conference on Computer Vision(ECCV), Prague, Czech Republic, May 2004.
[3]  D.Lowe. Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91–110.
[4]  H. Bay, T. Tuytelaars, and L. V. Gool. SURF: Speeded Up Robust Features. The 9th European Conference on Computer Vision, Austria, May 2006.
[5]  Gabriel Takacs, Vijay Chandrasekhar, Sam Tsai, et al. Unified Real-Time Tracking and Recognition with Rotation-Invariant Fast Features. IEEE Computer Con-ference on Computer Vision and Pattern Recognition (CVPR), 2010.
[6]  M. Calonder, V. Lepetit, C. Strecha, et al. Brief: Binary robust independent elementary features. The 11th European Conference on Computer Vision, 2010.
[7]  Ethan Rublee, Vincent Rabaud, Kurt Konolige, et al. ORB: an efficient alternative to SIFT or SURF. 2011 International Conference on Computer Vision(ICCV), 2011.
[8]  Khvedchenya Eugene. Feature descriptor comparison report. (2011-08-19)[2012-04-20].
[9]  Yilmaz A, Javed O, Shah M. Object tracking: A survey. ACM Computing Surveys, 2006, 38(4):45.
[10]  C. Tomasi and T. Kanade. Detection and tracking of point features. Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991.
[11]  Mian A S. Realtime visual tracking of aircrafts. Procee-dings of the 2008 Digital Image Computing: Techniques and Applications, USA, December 2008.
[12]  Ben Benfold, Ian Reid. Stable Multi-Target Tracking in Real-Time Surveillance Video. IEEE Computer Con-ference on Computer Vision and Pattern Recognition. 2011.
[13]  Gong Jian, Liu Fuqiang, Song Chunlin, et al. Research on the Moving Vehicle Detection Algorithm Based on the Motion Vector. Instrumentation, Measurement, Cir-cuits and Systems. 2012, AISC 127: 41–49.
[14]  Liu Y, Wang J D, Li P. A Feature Point Tracking Method Based on The Combination of SIFT Algorithm and KLT Matching Algorithm. Journal of Astronautics, 2011, 32(7): 1618-1625. (in Chinese)
[15]  刘玉, 王敬东, 李鹏. 一种基于SIFT 和KLT 相结合的特征点跟踪方法研究. 宇航学报, 2011, 32(7): 1618
[16]  -1625.
[17]  Wang Y M, Wang G J. Image Local Invariant Features and Descriptors. Beijing: National Defense Industry Press, 2010: 89-100. (in Chinese)
[18]  王永明, 王贵锦. 图像局部不变性特征与描述. 北京: 国防工业出版社, 2010: 89-100.
[19]  Song L H. The study of target positioning technology based on UAV image sequence. The Institute of Surveing and Mapping, PLA Information Engineering University, 2011. (in Chinese)
[20]  宋丽华. 基于无人飞行器序列影像的定位技术研究. 郑州: 解放军信息工程大学测绘学院, 2011.
[21]  Gary Bradski, Adrian Kaebler. Learning OpenCV. Yu SH Q, Liu R ZH, translated. Beijing: Tsinghua University Press, 2009: 362-363. (in Chinese)
[22]  Gary Bradski, Adrian Kaebler. 学习Opencv. 于仕琪, 刘瑞祯, 译. 北京: 清华大学出版社, 2009: 362-363.

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