An Innovative SIFT-Based Method for Rigid Video Object Recognition. (6th July 2014)

• Record Type: Journal Article
• Authors: Yu, Jie; Zhang, Fengli; Xiong, Jian
• Other Names: Hsieh Jer-Guang Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-07-06
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/138927
• Languages: English
• ISSNs: 1024-123X

Abstract

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.