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An Innovative SIFT-Based Method for Rigid Video Object Recognition

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  • Jie Yu
  • Fengli Zhang
  • Jian Xiong

Abstract

This paper presents an innovative SIFT-based method for rigid video object recognition (hereafter called RVO-SIFT). Just like what happens in the vision system of human being, this method makes the object recognition and feature updating process organically unify together, using both trajectory and feature matching, and thereby it can learn new features not only in the training stage but also in the recognition stage, which can improve greatly the completeness of the video object’s features automatically and, in turn, increases the ratio of correct recognition drastically. The experimental results on real video sequences demonstrate its surprising robustness and efficiency.

Suggested Citation

  • Jie Yu & Fengli Zhang & Jian Xiong, 2014. "An Innovative SIFT-Based Method for Rigid Video Object Recognition," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, July.
    • Handle: RePEc:hin:jnlmpe:138927
    DOI: 10.1155/2014/138927
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