NDLI: Robust object tracking via online Principal Component–Canonical Correlation Analysis (P3CA)

Content Provider	Springer Nature Link
Author	Wang, Yuxia Zhao, Qingjie
Copyright Year	2013
Abstract	Effective object representation plays a significant role in object tracking. To fulfill the requirements of tracking robustness and effectiveness, in this paper, we propose an adaptive appearance model called Principal Component–Canonical Correlation Analysis (P3CA). P3CA is a compact association of principal component analysis (PCA) and canonical correlation analysis (CCA), which results in robust tracking along with low computation cost. CCA is incorporated into P3CA appearance model for its effectiveness in handling occlusion due to the introduction of canonical correlation score instead of holistic information to evaluate the target goodness. However, it is time consuming and often suffers from Small Sample Size (3S) problem. To address these issues, PCA is incorporated and we obtain our P3CA subspace by performing CCA on the low-dimensional data gained by projecting the high-dimensional observations to the PCA subspaces. In addition, to account for appearance variations, we propose a novel online updating algorithm for P3CA subspace, which updates the PCA and CCA subspaces cooperatively and synchronously. Finally, we incorporate the dynamic P3CA appearance model with the particle filter framework in a probabilistic manner and select the candidate object with the largest weight as the final tracking result. Comparative results on several challenging sequences demonstrate that our tracker performs better than a number of state-of-the-art methods proposed recently in handling partial occlusion and various appearance variations.
Starting Page	159
Ending Page	174
Page Count	16
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	9
Issue Number	1
e-ISSN	18631711
Language	English
Publisher	Springer London
Publisher Date	2013-02-08
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Principal Component–Canonical Correlation Analysis Small Sample Size problem Adaptive appearance model Particle filtering Visual tracking Signal, Image and Speech Processing Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Multimedia Information Systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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