An Innovative SIFT-Based Method for Rigid Video Object Recognition | Zendy

Jie Yu | Zendy; Fengli Zhang | Zendy; Jian Xiong | Zendy

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An Innovative SIFT-Based Method for Rigid Video Object Recognition

Author(s) -

Jie Yu,

Fengli Zhang,

Jian Xiong

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/138927

Subject(s) - scale invariant feature transform , artificial intelligence , computer vision , computer science , robustness (evolution) , cognitive neuroscience of visual object recognition , matching (statistics) , 3d single object recognition , object (grammar) , feature (linguistics) , video tracking , feature matching , pattern recognition (psychology) , feature extraction , mathematics , biochemistry , chemistry , statistics , linguistics , philosophy , gene

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

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