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In this paper we propose an original approach to solve the Inverse Kinematics problem. Our framework is based on Sequential Monte Carlo Methods and has the advantage to avoid the classical pitfalls of numerical inversion methods since only direct calculations are required. The resulting algorithm accepts arbitrary constraints and exhibits linear complexity with respect to the number of degrees of...
The problem of modelling objects of arbitrary complecity for video based rendering has been much studied in recent years, with the growing interest in ‘free viewpoint’ video and similar applications. Typical approaches fall into two categories: those which approximate surfaces from dense depth maps obtained by generalisations of stereopsis methods and those which employ an explicit geometric representation...
We present a system that can segment articulated, non-rigid motion without a priori knowledge of the number of clusters present in the analyzed scenario. We combine existing algorithms for tracking and extend clustering techniques by a self-tuning heuristic. Application to video sequences of humans shows good segmentation into limbs.
We describe a hierarchical approach for recognizing continuous hand gestures. It consists of hierarchical nonlinear dimensionality reduction based feature extraction and Hierarchical Conditional Random Field (Hierarchical CRF) based motion modeling. Articulated hands can be decomposed into several hand parts and we explore the underlying structures of articulated action spaces for both the hand and...
We present a human body motion tracking system for an interactive virtual simulation training environment. This system captures images using IR illumination and near-IR cameras to overcome limitations of a dimly lit environment. Features, such as silhouettes and medial axis of blobs are extracted from the images which lack much internal texture. We use a combination of a 2D ICP and particle filtering...
This paper presents comparative results of applying different architectures of generative classifiers (HMM, FHMM, CHMM, Multi-Stream HMM, Parallel HMM ) and discriminative classifier as Conditional Random Fields (CRFs) in human action sequence recognition. The models are fed with histogram of very informative features such as contours evolution and optical-flow. Motion orientation discrimination has...
Many real-time algorithms for mesh deformation driven by animation of an underlying skeleton make use of a set of per-bone weights associated with each vertex. There are few unguided algorithms for the assignment of these weights with a recent proposed solution being bone heat [1]. In this paper, we briefly discuss bone heat and provide examples where it performs poorly. We then develop a refinement...
This paper describes a method to analyze human motion, based on the reduction of multidimensional captured motion data. A Dynamic Programming Piecewise Linear Approximation model is used to automatically extract in an optimal way key-postures distributed along the motion data. This non uniform sub-sampling can be exploited for motion compression, segmentation, or re-synthesis. It has been applied...
This paper presents a general analysis framework towards exploiting the underlying hierarchical and scalable structure of an articulated object for pose estimation and tracking. The Scalable Human Body Model (SHBM) is presented as a set of human body models ordered following a hierarchy criteria. The concept of annealing is applied to derive a generic particle filtering scheme able to perform a sequential...
This paper is concerned with real-time approaches to using marker-based optical motion capture to identify, parametrize, and estimate the frame by frame configuration of the human skeleton. An overview of the stages of a system is provided with the main emphasis devoted to two new methods for refining the rotation estimates used within the transformational algorithm class of joint parameter estimation...
Self-occlusion is a common problem in silhouette based motion capture, which often results in ambiguous pose configurations. In most works this is compensated by a priori knowledge about the motion or the scene, or by the use of multiple cameras. Here we suggest to overcome this problem by splitting the surface model of the object and tracking the silhouette of each part rather than the whole object...
Time-varying mesh (TVM) is a technique that describes full shape and motion of a real-world moving object. Thus, TVM is used to capture and reproduce human behavior and natural movements precisely, such as the expression of the face or small changes in cloths. But on the other hand, TVM requires large storage space and computational cost. To solve this problem, we propose a framework of motion editing...
This paper presents a method of interpolating between two or more general displacements (rotation and translation). The resulting interpolated path is smooth and possesses a number of desirable properties. It differs from existing algorithms which require factorising the pose into separate rotation and translation components and is derived from an intuitively appealing framework–i.e. a natural extension...
We present a methodology developed to derive three-dimensional models of speech articulators from volume MRI and multiple view video images acquired on one speaker. Linear component analysis is used to model these highly deformable articulators as the weighted sum of a small number of basic shapes corresponding to the articulators’ degrees of freedom for speech. These models are assembled into an...
Cloth simulation is an extremely expensive task. Realistic cloth models coupled with stable numerical integration demand all the processing power we can spend. Although implicit integration schemes allow us to use large time steps, the exponential time complexity limits the number of particles that are reasonable to use in any simulation. In this paper, we present a technique that simulates cloth...
Recently many methods for human articulated body tracking were proposed in the literature. These techniques are often computationally intensive and cannot be used for Human-Computer Interface. We propose in this article a real-time algorithm for upper body tracking with occultation handling. The tracking is based on an articulated body model, also used to automatically initialize the target. After...
This paper proposes a two-step approach for detecting individuals within dense crowds. First step uses an offline-trained Viola-type head detector in still color images of dense crowds in a cluttered background. In the second step, which aims to reduce false alarm rates at same detection rates, color bin images are constructed from normalized rg color histograms of the detected windows in the first...
This paper presents an efficient BSpline surface reconstruction technique for modelling deformable objects. The differences of our methods from previous BSpline fitting approaches are: 1) the reconstructed BSpline patch does not need to be square shaped. This significantly reduces the required number of BSpline patches for reconstruction; 2) the dataset to be reconstructed does not have to be grid...
This paper presents a novel, agent-based sensing-system reconfigura tion methodology for the recognition of time-varying geometry objects or subjects (targets). A multi-camera active-vision system is used to improve form-recognition performance by selecting near-optimal viewpoints along a prediction horizon. The proposed method seeks to maximize the visibility of such a time-varying geometry in a...
This paper presents a framework for view-invariant action recognition in image sequences. Feature-based human detection becomes extremely challenging when the agent is being observed from different viewpoints. Besides, similar actions, such as walking and jogging, are hardly distinguishable by considering the human body as a whole. In this work, we have developed a system which detects human body...
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