Visual tracking via incremental self-tuning particle filtering on the affine group

We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group. SIFT (Scale Invariant Feature Transform) like descriptors are used as basic features, and IPCA (Incremental Principle Component Analysis) is utilized to learn an adaptive appearance sub...

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Bibliographic Details
Published in2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 1315 - 1322
Main Authors Min Li, Wei Chen, Kaiqi Huang, Tieniu Tan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Bayesian methods
Boosting
Cost function
Filtering
Laboratories
Particle tracking
Robustness
State estimation
Stochastic processes
Target tracking
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Summary:We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group. SIFT (Scale Invariant Feature Transform) like descriptors are used as basic features, and IPCA (Incremental Principle Component Analysis) is utilized to learn an adaptive appearance subspace for similarity measurement. ISPF tries to find the optimal target position in a step-by-step way: particles are incrementally drawn and intelligently tuned to their best states by an online LWPR (Local Weighted Projection Regression) pose estimator; searching is terminated if the maximum similarity of all tuned particles satisfies a target similarity distribution (TSD) modeled online or the permitted maximum number of particles is reached. Experimental results demonstrate that our ISPF can achieve great robustness and very high accuracy with only a very small number of random particles.
ISBN:1424469848
9781424469840
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2010.5539815