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
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Self-adjusting the intensity of assortative mating in genetic algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Model based human motion tracking using probability evolutionary algorithm
Pattern Recognition Letters
Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace
IEEE Transactions on Pattern Analysis and Machine Intelligence
Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Interactive animation of virtual humans based on motion capture data
Computer Animation and Virtual Worlds - International Workshop Motion in Games (MIG08)
International Journal of Computer Vision
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
Information Sciences: an International Journal
3D human motion tracking based on a progressive particle filter
Pattern Recognition
Markerless human articulated tracking using hierarchical particle swarm optimisation
Image and Vision Computing
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Markerless Human Motion Capture is the problem of determining the joints' angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem of Markerless Human Motion Capture is high-dimensional and requires the use of models with a considerable number of degrees of freedom to appropriately adapt to the human anatomy. Particle filters have become the most popular approach for Markerless Human Motion Capture, despite their difficulty to cope with high-dimensional problems. Although several solutions have been proposed to improve their performance, they still suffer from the curse of dimensionality. As a consequence, it is normally required to impose mobility limitations in the body models employed, or to exploit the hierarchical nature of the human skeleton by partitioning the problem into smaller ones. Evolutionary algorithms, though, are powerful methods for solving continuous optimization problems, specially the high-dimensional ones. Yet, few works have tackled Markerless Human Motion Capture using them. This paper evaluates the performance of three of the most competitive algorithms in continuous optimization - Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization - with two of the most relevant particle filters proposed in the literature, namely the Annealed Particle Filter and the Partitioned Sampling Annealed Particle Filter. The algorithms have been experimentally compared in the public dataset HumanEva-I by employing two body models with different complexities. Our work also analyzes the performance of the algorithms in hierarchical and holistic approaches, i.e., with and without partitioning the search space. Non-parametric tests run on the results have shown that: (i) the evolutionary algorithms employed outperform their particle filter counterparts in all the cases tested; (ii) they can deal with high-dimensional models thus leading to better accuracy; and (iii) the hierarchical strategy surpasses the holistic one.