Object tracking by exploiting adaptive region-wise linear subspace representations and adaptive templates in an iterative particle filter

Authors:
Ming-Che Ho;Cheng-Chin Chiang;Ying-Yu Su
Affiliations:
Department of Computer Science and Information Engineering, National Dong Hwa University, Shoufeng, Hualien 974, Taiwan;Department of Computer Science and Information Engineering, National Dong Hwa University, Shoufeng, Hualien 974, Taiwan;Department of Computer Science and Information Engineering, National Dong Hwa University, Shoufeng, Hualien 974, Taiwan
Venue:
Pattern Recognition Letters
Year:
2012

Citing 14
Cited 0

EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation

International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density

ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Incremental Singular Value Decomposition of Uncertain Data with Missing Values

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Candid Covariance-Free Incremental Principal Component Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Lucas-Kanade 20 Years On: A Unifying Framework

International Journal of Computer Vision
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Incremental Learning for Robust Visual Tracking

International Journal of Computer Vision
A Rao-Blackwellized particle filter for EigenTracking

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Sequential Karhunen-Loeve basis extraction and its application to images

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.11

Visualization

Abstract

Aiming at tracking visual objects under harsh conditions, such as partial occlusions, illumination changes, and appearance variations, this paper proposes an iterative particle filter incorporated with an adaptive region-wise linear subspace (RWLS) representation of objects. The iterative particle filter employs a coarse-to-fine scheme to decisively generate particles that convey better hypothetic estimates of tracking parameters. As a result, a higher tracking accuracy can be achieved by aggregating the good hypothetic estimates from particles. Accompanying with the iterative particle filter, the RWLS representation is a special design to tackle the partial occlusion problem which often causes tracking failure. Moreover, the RWLS representation is made adaptive by exploiting an efficient incremental updating mechanism. This incremental updating mechanism can adapt the RWLS to gradual changes in object appearances and illumination conditions. Additionally, we also propose the adaptive mechanism to continuously adjust the object templates so that the varying appearances of tracked objects can be well handled. Experimental results demonstrate that the proposed approach achieves better performance than other related prior arts.