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Mathematical Problems in Engineering
Volume 2014, Article ID 138927, 11 pages
Research Article

An Innovative SIFT-Based Method for Rigid Video Object Recognition

1School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chendu, Sichuan 611731, China
2Mathematics and Computer Science Department, Shangrao Normal University, No. 85, Zhiming Avenue, Shangrao, Jiangxi 334001, China

Received 11 April 2014; Revised 5 June 2014; Accepted 9 June 2014; Published 6 July 2014

Academic Editor: Jer-Guang Hsieh

Copyright © 2014 Jie Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.