Target Recognition and Tracking Method Based on Multiple Templates in Complex Scenes

Conference paper
First Online:
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 212)

Abstract

Image matching is a key technology in target recognition and tracking systems. This paper presents a method based on multi-template replacement strategy. The proposed method can ensure the resolution and accuracy of target recognition regardless of distance and field of view, and uses the idea of the Bayesian classifier. The problem of target recognition is converted into solving the Bayesian class posterior probability. This method was found to have high real-time performance and high target recognition accuracy.

Keywords

Bayesian classifier Image processing Multiple templates Offline training 
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References

  1. 1.
    Crimmings TR (1982) A complete set of Fourier descriptors for two-dimensional shapes. IEEE Trans Syst Man Cybernet SMC 12(6):845–855Google Scholar
  2. 2.
    Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8:179–187MATHGoogle Scholar
  3. 3.
    Flusser J, Suk T (1993) Pattern recognition by affine moment invariants. Pattern Recogn 26(1):167–174MathSciNetCrossRefGoogle Scholar
  4. 4.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110CrossRefGoogle Scholar
  5. 5.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: International conference on computer vision, Corfu, Greece, pp 1150–1155Google Scholar
  6. 6.
    Mikolajczyk K, Schmid CA (2005) Performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630CrossRefGoogle Scholar
  7. 7.
    Bay H, Tuytelaars T (2008) SURF: speeded up robust features. Comput Vis Image Underst 110(3):346–359CrossRefGoogle Scholar
  8. 8.
    Lepetit V, Fua P (2006) Keypoint recognition using randomized trees. IEEE Trans Pattern Anal Mach Intell 28(9):1465–1479CrossRefGoogle Scholar
  9. 9.
    Dong LY, Yuan SM, Liu GY (2007) Image classification based on Bayesian classifier. J Jilin University 45(2):249–253Google Scholar
  10. 10.
    Zheng F, Webb G (2005) A comparative study of semi-naive Bayes methods in classification learning. In: Proceedings of the fourth Australasian data mining conference (AusDM05), pp 141–156Google Scholar
  11. 11.
    Rosten E, Durmmond T (2006) Machine learning for high speed corner detection. In: 9th European conference on computer vision, vol 3951(1), pp 430-443Google Scholar
  12. 12.
    Rosten E, Porter R, Drummond T (2008) Faster and better: a machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell 32(1):105–119CrossRefGoogle Scholar
  13. 13.
    Fisehler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhifeng Gao
    • 1
  • Bo Wang
    • 1
  • Mingjie Dong
    • 1
  • Yongsheng Shi
    • 1
  1. 1.School of Automation, Beijing Institute of TechnologyBeijingChina

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