3D human motion tracking based on a progressive particle filter

Authors:
I-Cheng Chang;Shih-Yao Lin
Affiliations:
Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan;Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan
Venue:
Pattern Recognition
Year:
2010

Citing 10
Cited 7

CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Particle Filter with Analytical Inference for Human Body Tracking

MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Multiple Object Tracking with Kernel Particle Filter

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Using Interval Particle Filtering for Marker Less 3D Human Motion Capture

ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Kernel Particle Filter for Real-Time 3D Body Tracking in Monocular Color Images

FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Shape and motion driven particle filtering for human body tracking

ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Tracking 3D Human Body using Particle Filter in Moving Monocular Camera

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Real time hand tracking by combining particle filtering and mean shift

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition

Recognizing multiple human activities and tracking full-body pose in unconstrained environments

Pattern Recognition
The different possibilities for gait identification based on motion capture

EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part II
Dynamic appearance model for particle filter based visual tracking

Pattern Recognition
Parametric annealing: A stochastic search method for human pose tracking

Pattern Recognition
Part template: 3D representation for multiview human pose estimation

Pattern Recognition
Design of an efficient framework for fast prototyping of customized human-computer interfaces and virtual environments for rehabilitation

Computer Methods and Programs in Biomedicine
Comparing evolutionary algorithms and particle filters for Markerless Human Motion Capture

Applied Soft Computing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Human body tracking has received increasing attention in recent years due to its broad applicability. Among these tracking algorithms, the particle filter is considered an effective approach for human motion tracking. However, it suffers from the degeneracy problem and considerable computational burden. This paper presents a novel 3D model-based tracking algorithm called the progressive particle filter to decrease the computational cost in high degrees of freedom by employing hierarchical searching. In the proposed approach, likelihood measure functions involving four different features are presented to enhance the performance of model fitting. Moreover, embedded mean shift trackers are adopted to increase accuracy by moving each particle toward the location with the highest probability of posture through the estimated mean shift vector. Experimental results demonstrate that the progressive particle filter requires lower computational cost and delivers higher accuracy than the standard particle filter.