Adaptive particle filter based on energy field for robust object tracking in complex scenes

Authors:
Xin Sun;Hongxun Yao;Shengping Zhang;Shaohui Liu
Affiliations:
Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Year:
2010

Citing 11
Cited 0

Efficient Region Tracking With Parametric Models of Geometry and Illumination

IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Localization by Bayesian Correlation

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Fast Multiple Object Tracking via a Hierarchical Particle Filter

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Stochastic Filter for Fluid Motion Tracking

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Differential Tracking based on Spatial-Appearance Model (SAM)

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Toward Optimal Kernel-based Tracking

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Fast stochastic optimization for articulated structure tracking

Image and Vision Computing
3D shape-encoded particle filter for object tracking and its application to human body tracking

Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Particle filter (PF) based object tracking methods have been widely used in computer vision. However, traditional particle filter trackers cannot effectively distinguish the target from the background in complex scenes since they only exploit appearance information of observation to determine the target region. In this paper, we present an adaptive particle filter based on energy field (EPF), which makes good use of moving information of previous frames adaptively to track the target. Besides, we present the mechanism of result rectification to ensure the target region is accurate. Experiment results on several challenging video sequences have verified that the adaptive EPF method is compared very robust and effective with the traditional particle filter in many complicated scenes.