4,484 research outputs found

    Using Interval Particle Filtering for Marker less 3D Human Motion Capture

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    In this paper we present a new approach for marker less human motion capture from conventional camera feeds. The aim of our study is to recover 3D positions of key points of the body that can serve for gait analysis. Our approach is based on foreground segmentation, an articulated body model and particle filters. In order to be generic and simple no restrictive dynamic modelling was used. A new modified particle filtering algorithm was introduced. It is used efficiently to search the model configuration space. This new algorithm which we call Interval Particle Filtering reorganizes the configurations search space in an optimal deterministic way and proved to be efficient in tracking natural human movement. Results for human motion capture from a single camera are presented and compared to results obtained from a marker based system. The system proved to be able to track motion successfully even in partial occlusions

    Online Discrimination of Nonlinear Dynamics with Switching Differential Equations

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    How to recognise whether an observed person walks or runs? We consider a dynamic environment where observations (e.g. the posture of a person) are caused by different dynamic processes (walking or running) which are active one at a time and which may transition from one to another at any time. For this setup, switching dynamic models have been suggested previously, mostly, for linear and nonlinear dynamics in discrete time. Motivated by basic principles of computations in the brain (dynamic, internal models) we suggest a model for switching nonlinear differential equations. The switching process in the model is implemented by a Hopfield network and we use parametric dynamic movement primitives to represent arbitrary rhythmic motions. The model generates observed dynamics by linearly interpolating the primitives weighted by the switching variables and it is constructed such that standard filtering algorithms can be applied. In two experiments with synthetic planar motion and a human motion capture data set we show that inference with the unscented Kalman filter can successfully discriminate several dynamic processes online

    Markerless Human Motion Capture for Gait Analysis

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    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

    Factored Interval Particle Filtering for Gait Analysis

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    Scheduled for presentation during the Poster Session "Signal Pattern Classification in Biomedical Signals V" (FrP2A1)International audienceCommercial gait analysis systems rely on wearable sensors. The goal of this study is to develop a low cost marker less human motion capture tool. Our method is based on the estimation of 3d movements using video streams and the projection of a 3d human body model. Dynamic parameters only depend on human body movement constraints. No trained gait model is used which makes this approach generic. The 3d model is characterized by the angular positions of its articulations. The kinematic chain structure allows to factor the state vector representing the conguration of the model. We use a dynamic bayesian network and a modied particle filtering algorithm to estimate the most likely state conguration given an observation sequence. The modied algorithm takes advantage of the factorization of the state vector for efciently weighting and resampling the particles

    Models and estimators for markerless human motion tracking

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    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

    Reducing Particle Filtering Complexity for 3D Motion Capture using Dynamic Bayesian Networks

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    International audienceParticle filtering algorithms can be used for the monitoring of dynamic systems with continuous state variables and without any constraints on the form of the probability distributions. The dimensionality of the problem remains a limitation of these approaches due to the growing number of particles required for the exploration of the state space. Computer vision problems such as 3D motion tracking are an example of complex monitoring problems which have a high dimensional state space and observation functions with high computational cost. In this article we focus on reducing the required number of particles in the case of monitoring tasks where the state vector and the observation function can be factored. We introduce a particle filtering algorithm based on the dynamic Bayesian network formalism which takes advantage of a factored representation of the state space for efficiently weighting and selecting the particles. We illustrate the approach on a simulated and a realworld 3D motion tracking tasks

    Configurable Input Devices for 3D Interaction using Optical Tracking

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    Three-dimensional interaction with virtual objects is one of the aspects that needs to be addressed in order to increase the usability and usefulness of virtual reality. Human beings have difficulties understanding 3D spatial relationships and manipulating 3D user interfaces, which require the control of multiple degrees of freedom simultaneously. Conventional interaction paradigms known from the desktop computer, such as the use of interaction devices as the mouse and keyboard, may be insufficient or even inappropriate for 3D spatial interaction tasks. The aim of the research in this thesis is to develop the technology required to improve 3D user interaction. This can be accomplished by allowing interaction devices to be constructed such that their use is apparent from their structure, and by enabling efficient development of new input devices for 3D interaction. The driving vision in this thesis is that for effective and natural direct 3D interaction the structure of an interaction device should be specifically tuned to the interaction task. Two aspects play an important role in this vision. First, interaction devices should be structured such that interaction techniques are as direct and transparent as possible. Interaction techniques define the mapping between interaction task parameters and the degrees of freedom of interaction devices. Second, the underlying technology should enable developers to rapidly construct and evaluate new interaction devices. The thesis is organized as follows. In Chapter 2, a review of the optical tracking field is given. The tracking pipeline is discussed, existing methods are reviewed, and improvement opportunities are identified. In Chapters 3 and 4 the focus is on the development of optical tracking techniques of rigid objects. The goal of the tracking method presented in Chapter 3 is to reduce the occlusion problem. The method exploits projection invariant properties of line pencil markers, and the fact that line features only need to be partially visible. In Chapter 4, the aim is to develop a tracking system that supports devices of arbitrary shapes, and allows for rapid development of new interaction devices. The method is based on subgraph isomorphism to identify point clouds. To support the development of new devices in the virtual environment an automatic model estimation method is used. Chapter 5 provides an analysis of three optical tracking systems based on different principles. The first system is based on an optimization procedure that matches the 3D device model points to the 2D data points that are detected in the camera images. The other systems are the tracking methods as discussed in Chapters 3 and 4. In Chapter 6 an analysis of various filtering and prediction methods is given. These techniques can be used to make the tracking system more robust against noise, and to reduce the latency problem. Chapter 7 focusses on optical tracking of composite input devices, i.e., input devices 197 198 Summary that consist of multiple rigid parts that can have combinations of rotational and translational degrees of freedom with respect to each other. Techniques are developed to automatically generate a 3D model of a segmented input device from motion data, and to use this model to track the device. In Chapter 8, the presented techniques are combined to create a configurable input device, which supports direct and natural co-located interaction. In this chapter, the goal of the thesis is realized. The device can be configured such that its structure reflects the parameters of the interaction task. In Chapter 9, the configurable interaction device is used to study the influence of spatial device structure with respect to the interaction task at hand. The driving vision of this thesis, that the spatial structure of an interaction device should match that of the task, is analyzed and evaluated by performing a user study. The concepts and techniques developed in this thesis allow researchers to rapidly construct and apply new interaction devices for 3D interaction in virtual environments. Devices can be constructed such that their spatial structure reflects the 3D parameters of the interaction task at hand. The interaction technique then becomes a transparent one-to-one mapping that directly mediates the functions of the device to the task. The developed configurable interaction devices can be used to construct intuitive spatial interfaces, and allow researchers to rapidly evaluate new device configurations and to efficiently perform studies on the relation between the spatial structure of devices and the interaction task
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