Particle Filter Based Object Tracking with Discriminative Feature Extraction and Fusion

Authors:
Yao Shen;Parthasarathy Guturu;Thyagaraju Damarla;Bill P. Buckles
Affiliations:
University of North Texas, Denton, USA TX 76207;University of North Texas, Denton, USA TX 76207;Army Research Lab, Adelphi, USA MD 20783;University of North Texas, Denton, USA TX 76207
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Year:
2008

Citing 8
Cited 0

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation

ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
View-Based Adaptive Affine Tracking

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Towards Improved Observation Models for Visual Tracking: Selective Adaptation

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
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking

IEEE Transactions on Image Processing
Adaptive Multifeature Tracking in a Particle Filtering Framework

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an object tracking algorithm based on the unscented particle filtering (UPF) approach. In this algorithm, occlusion tolerant features are first obtained for the images of the object in the consecutive frames based on the color, texture and shape (edge) information, and then a variant of the Fisher's linear discriminant function approach is applied for reducing the dimensionality of the feature space. Similarities of the two images in each feature dimension are computed by matching the histograms of the quantized feature values, and finally these similarity values are aggregated into an over all similarity measure by a novel feature fusion technique embedded in the UPF framework. Results of experimentation with two different data sets indicate that our algorithm is both efficacious in handling severe occlusions (almost as high as 80%) and efficient with respect to tracking accuracy ...