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

Tracking-as-recognition for articulated full-body human motion analysis

This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action\u27s motions are modelled with a variant of the hierarchical hidden Markov model The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.<br /

Real-Time 3D Articulated Pose Tracking using Particle Filtering and Belief Propagation on Factor Graphs

This article proposes a new statistical model for fast 3D articulated body tracking, similar to the loose-limbed model, but using the factor graph representation. Belief propagation is used to estimate the current marginal for each limb. All belief propagation messages are represented as sums of weighted samples. The resulting algorithm corresponds to a set of particle filters, one for each limb, where an extra step recomputes the weight of each sample by taking into account the interactions between limbs. To take into account fast moving limbs, proposal maps are used to steer samples to regions of high likelihood. Applied to upper-body tracking with disparity and colour images, the resulting algorithm estimates the body pose in quasi real-time (10Hz).

Real-time 3-d human body tracking using variable length markov models

In this paper, we introduce a 3-D human-body tracker capable of handling fast and complex motions in real-time. The parameter space, augmented with first order derivatives, is automatically partitioned into Gaussian clusters each representing an elementary motion: hypothesis propagation inside each cluster is therefore accurate and efficient. The transitions between clusters use the predictions of a Variable Length Markov Model which can explain highlevel behaviours over a long history. Using Monte-Carlo methods, evaluation of model candidates is critical for both speed and robustness. We present a new evaluation scheme based on volumetric reconstruction and blobs-fitting, where appearance models and image evidences are represented by Gaussian mixtures. We demonstrate the application of our tracker to long video sequences exhibiting rapid and diverse movements.

Feature-based annealing particle filter for robust motion capture

This thesis presents a new annealing method for particle filtering aiming at body pose estimation. Particle filters are Monte Carlo methods commonly employed in non-linear and non-Gaussian Bayesian problems, such as the estimation of human dynamics. However, they are ine±cient in high-dimensional state spaces. Annealed particle filter copes with such spaces by introducing a layered stochastic search. Our algorithm aims at generalizing and enhancing the classical annealed particle filter. Diferent image features are exploited in a sequential importance sampling scheme to build better proposal distributions from likelihood. This technique, termed Feature-Based Annealing, is inferred from the required function properties in the annealing process and the properties of the weighting functions obtained with common image features in the field of body tracking. Comparative results between the proposed strategy and common annealed particle filter are shown to assess the robustness of the algorithm

Modelling, tracking and generating human interaction behaviours in video

Intelligent virtual characters are becoming increasingly popular in en­ tertainment, educational and simulation software. A virtual charac­ ter is the creation or re-creation of a human being in an image, using computer-generated imagery. It must act and react in the environment, drawing on the disciplines of automated reasoning and planning. Creating characters with human-like behaviours that respond interactively to a real person in a video, is still a serious challenge. There are several major reasons for this. First, human motion is very complex, which makes it particularly difficult to simulate. Second, the human form is also not straightforward to design due to the large number of degrees of freedom of the motion. Third, creating novel contextual movements for virtual characters in real time is a new research area.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modelling, tracking and generating human interaction behaviours in video.

Intelligent virtual characters are becoming increasingly popular in en­ tertainment, educational and simulation software. A virtual charac­ ter is the creation or re-creation of a human being in an image, using computer-generated imagery. It must act and react in the environment, drawing on the disciplines of automated reasoning and planning. Cre­ ating characters with human-like behaviours that respond interactively to a real person in a video, is still a serious challenge. There are several major reasons for this. First, human motion is very complex, which makes it particularly difficult to simulate. Second, the human form is also not straightforward to design due to the large number of degrees of freedom of the motion. Third, creating novel contextual movements for virtual characters in real time is a new research area