Kernel-Based On-Line Object Tracking Combining both Local Description and Global Representation

Quan MIAO  Guijin WANG  Xinggang LIN  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.1   pp.159-162
Publication Date: 2013/01/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.159
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
object tracking,  keypoint matching,  kernel function,  classifier updating,  

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This paper proposes a novel method for object tracking by combining local feature and global template-based methods. The proposed algorithm consists of two stages from coarse to fine. The first stage applies on-line classifiers to match the corresponding keypoints between the input frame and the reference frame. Thus a rough motion parameter can be estimated using RANSAC. The second stage employs kernel-based global representation in successive frames to refine the motion parameter. In addition, we use the kernel weight obtained during the second stage to guide the on-line learning process of the keypoints' description. Experimental results demonstrate the effectiveness of the proposed technique.