451 research outputs found
Models and estimators for markerless human motion tracking
In this work, we analyze the diferent components of a model-based motion tracking system.
The system consists in: a human body model, an estimator, and a likelihood or cost
function
Virtual friend: tracking and generating natural interactive behaviours in real video
The aim of our research is to create a âvirtual
friendâ i.e., a virtual character capable of responding
to actions obtained from observing a real person in
video in a realistic and sensible manner. In this paper,
we present a novel approach for generating a variety
of complex behavioural responses for a fully articulated
âvirtual friendâ in three dimensional (3D) space.
Our approach is model-based. First of all, we train a
collection of dual Hidden Markov Models (HMMs) on
3D motion capture (MoCap) data representing a number
of interactions between two people. Secondly, we
track 3D articulated motion of a single person in
ordinary 2D video. Finally, using the dual HMM, we
generate a moving âvirtual friendâ reacting to the
motion of the tracked person and place it in the
original video footage. In this paper, we describe our
approach in depth as well as present the results of
experiments, which show that the produced behaviours
are very close to those of real people
Voxel based annealed particle filtering for markerless 3D articulated motion capture
This paper presents a view-independent approach to markerless human motion capture in low resolution sequences from multiple calibrated and synchronized cameras. Redundancy among cameras is exploited to generate a 3D voxelized representation of the scene and a human body model (HBM) is introduced towards analyzing these data. An annealed particle filtering scheme where every particle encodes an instance of the pose of the HBM is employed. Likelihood
between particles and input data is performed using occupancy and surface information and kinematic constrains are imposed in the propagation step towards avoiding impossible poses. Test over the
HumanEva annotated dataset yield quantitative results showing the
effectiveness of the proposed algorithm.Postprint (published version
Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications
Interacting and annealing are two powerful strategies that are applied in different areas of stochastic modelling and data analysis. Interacting particle systems approximate a distribution of interest by a finite number of particles where the particles interact between the time steps. In computer vision, they are commonly known as particle filters. Simulated annealing, on the other hand, is a global optimization method derived from statistical mechanics. A recent heuristic approach to fuse these two techniques for motion capturing has become known as annealed particle filter. In order to analyze these techniques, we rigorously derive in this paper two algorithms with annealing properties based on the mathematical theory of interacting particle systems. Convergence results and sufficient parameter restrictions enable us to point out limitations of the annealed particle filter. Moreover, we evaluate the impact of the parameters on the performance in various experiments, including the tracking of articulated bodies from noisy measurements. Our results provide a general guidance on suitable parameter choices for different applications
Markerless Human Motion Capture for Gait Analysis
The aim of our study is to detect balance disorders and a tendency towards
the falls in the elderly, knowing gait parameters. In this paper we present a
new tool for gait analysis based on markerless human motion capture, from
camera feeds. The system introduced here, recovers the 3D positions of several
key points of the human body while walking. Foreground segmentation, an
articulated body model and particle filtering are basic elements of our
approach. No dynamic model is used thus this system can be described as generic
and simple to implement. A modified particle filtering algorithm, which we call
Interval Particle Filtering, is used to reorganise and search through the
model's configurations search space in a deterministic optimal way. This
algorithm was able to perform human movement tracking with success. Results
from the treatment of a single cam feeds are shown and compared to results
obtained using a marker based human motion capture system
Combination of Annealing Particle Filter and Belief Propagation for 3D Upper Body Tracking
3D upper body pose estimation is a topic greatly studied by the computer vision society because it is useful in a great number of applications, mainly for human robots interactions including communications with companion robots. However there is a challenging problem: the complexity of classical algorithms that increases exponentially with the dimension of the vectorsâ state becomes too difficult to handle. To tackle this problem, we propose a new approach that combines several annealing particle filters defined independently for each limb and belief propagation method to add geometrical constraints between individual filters. Experimental results on a real human gestures sequence will show that this combined approach leads to reliable results
Feature-based annealing particle filter for robust body pose estimation
This paper presents a new annealing method for particle filtering in the context of body pose estimation. The
feature-based annealing is inferred from the weighting functions obtained with common image features used
for the likelihood approximation. We introduce a complementary weighting function based on the foreground
extraction and we balance the different measures through the annealing layers in order to improve the posterior
estimate. This technique is applied to estimate the upper body pose of a subject in a realistic multi-view
environment. Comparative results between the proposed method and the common annealing strategy are
presented to assess the robustness of the algorithm.Postprint (published version
Comparing Evolutionary Algorithms and Particle Filters for Markerless Human Motion Capture
Markerless Human Motion Capture is the problem of determining the jointsâ 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 â Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization â 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
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