On-Line Rigid Object Tracking via Discriminative Feature Classification

Quan MIAO  Chenbo SHI  Long MENG  Guang CHENG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.11   pp.2824-2827
Publication Date: 2016/11/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8098
Type of Manuscript: LETTER
Category: Pattern Recognition
Keyword: 
object tracking,  on-line boosting,  Gaussian mixture model,  self-organizing clustering,  

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Summary: 
This paper proposes an on-line rigid object tracking framework via discriminative object appearance modeling and learning. Strong classifiers are combined with 2D scale-rotation invariant local features to treat tracking as a keypoint matching problem. For on-line boosting, we correspond a Gaussian mixture model (GMM) to each weak classifier and propose a GMM-based classifying mechanism. Meanwhile, self-organizing theory is applied to perform automatic clustering for sequential updating. Benefiting from the invariance of the SURF feature and the proposed on-line classifying technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experiments show that the proposed method achieves better performance than previously reported trackers.