Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Real-Time 3D Body Pose Tracking from Multiple 2D Images
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Fast nonparametric belief propagation for real-time stereo articulated body tracking
Computer Vision and Image Understanding
International Journal of Computer Vision
Quasi Monte Carlo partitioned filtering for visual human motion capture
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Likelihood tuning for particle filter in visual tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Markerless human articulated tracking using hierarchical particle swarm optimisation
Image and Vision Computing
Contour cue based particle filter for monocular human motion tracking
Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications in Industry
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation
International Journal of Computer Vision
Motion Coherent Tracking Using Multi-label MRF Optimization
International Journal of Computer Vision
Data-Driven manifolds for outdoor motion capture
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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This paper presents the first systematic empirical study of the particle filter (PF) algorithms for human figure tracking in video. Our analysis and evaluation follows a modular approach which is based upon the underlying statistical principles and computational concerns that govern the performance of PF algorithms. Based on our analysis, we propose a novel PF algorithm for figure tracking with superior performance called the Optimized Unscented PF. We examine the role of edge and template features, introduce computationally-equivalent sample sets, and describe a method for the automatic acquisition of reference data using standard motion capture hardware. The software and test data are made publicly-available on our project website.