Multiple cues used in model-based human motion capture (original) (raw)

Modelling and estimating the pose of a human arm

Machine Vision and Applications, 2003

Pose estimation of 3-D objects based on monocular computer vision is an ill-posed problem. To ease matters a model-based approach can be applied. Such an approach usually relies on iterating when matching the model and the image data. In this paper we estimate the 3-D pose of a human arm from a monocular image. To avoid the inherent problems when iterating, we apply an exhaustive matching strategy. To make this plausible, we reduce the size of the solution space through a very compact model representation of the arm and prune the solution space. The model is developed through a detailed investigation of the functionality and structure of the arm and the shoulder complex. The model consists of just two parameters and is based on the screw-axis representation together with image measurements. The pruning is achieved through kinematic constraints and it turns out that the solution space of the compact model can be pruned 97%, on average. Altogether, the compact representation and the constraints reduce the solution space significantly and, therefore, allow for an exhaustive matching procedure. The approach is tested in a model-based silhouette framework, and tests show promising results.

Articulated three-dimensional human modelling from motion capture systems

2009

Human 3D models and motion analysis are nowadays used in a wide range of applications, spanning from medicine to security and surveillance. In this work, we will focus on the creation of biomechanical models for clinical and sports application of these methods. State of the art motion capture systems are capable of measuring with sufficient accuracy the 3D coordinates of reflective markers place above the skin. The main issues clinical gait analysis is currently facing are the repeatability of the measurements and the compensation of soft-tissue motion. Here we present a general framework to automatically recover joint parameters modelling the human articulations, from the 3D coordinates of a point cloud provided by motion capture systems. Additionally, we describe an approach capable of recovering a more accurate rigid body description of non-rigid bodies, and able to deal with the problem of marker occlusion. We then propose a new quadratic model to explain soft-tissue artifacts f...

Real-Time and Markerless 3D Human Motion Capture Using Multiple Views

2007

We present a fully automated system for real-time markerless 3D human motion capture. Our approach, based on fast algorithms, uses simple techniques and requires low-cost devices. Using input from multiple calibrated webcams, an extended Shape-From-Silhouette algorithm reconstructs the person in real-time. Fast 3D shape and 3D skin parts analysis provide a robust and real-time system for human full-body tracking. Animation skeleton and simple morphological constraints make easier the motion capture process. Thanks to fast and simple algorithms and low-cost cameras, our system is perfectly apt for home entertainment device. Results on real video sequences with complicated motions demonstrate the robustness of the approach.

Markerless monocular motion capture using image features and physical constraints

2005

We present a technique to extract motion parameters of a human figure from a single video stream. Our goal is to prototype motion synthesis rapidly for game design and animation applications. For example, our approach is especially useful in situations where motion capture systems are restricted in their usefuhess given the various required instrumentation. Similarly, our approach can be used to synthesize motion from archival footage. By extracting the silhouette of the foreground figure and using a model-based approach, the problem is re-formulated as a local, optimized search ofthe pose space, The pose space consists of 6 rigid body vansformation parameters plus the internal joint angles of the figure. The silhouette of the figure from the captured video is compared against the silhoueite of a synthetic figure using a pixel-bypixel, distancebased cost function to evaluate goodness-of-fit. For for a single video stream, this is not without problems. Occlusion and ambiguities arising from the use of a single view often cause spurious reconstruction of the captured motion. By using temporal coherence, physical constraints, and knowledge of the anatomy, a viable pose sequence can be reconstructed for many live-action sequences.

Towards model-based capture of a persons shape, appearance and motion

Proceedings IEEE International Workshop on Modelling People. MPeople'99

This paper introduces a model-based approach to capturing a persons shape, appearance and movement. A 3D animated model of a clothed persons whole-body shape and appearance is automatically constructed from a set of orthogonal view colour images. The reconstructed model of a person is then used together with the least-squares inverse-kinematics framework of Bregler et al. [4] to capture simple 3D movements from a video image sequence.

Pose Estimation Of A Human Arm Using Kinematic Constraints

Scandinavian Conference on Image Analysis, 2001

This paper is concerned with visual motion capture of a humanarm. The input is a colour image and the output is the3D pose of the arm in the current image. To handle the obviousambiguities involved in 3D estimation based on 2Ddata a model-based approach is adapted where all configurationsof the arm are matched with the image data. Theseconfigurations are efficiently

3D model based gesture acquisition using a single camera

… of Computer Vision, 2002.(WACV 2002). …, 2002

We present a method for 3D human motion capture using a single camera, without markers and without a priori knowledge on gestures. It is based on registering a 3D articulated model on color images with respect to biomechanical constraints. Gestures regularization is discussed as a way to cope with projection ambiguities. Computation is reduced by registering only the moving parts of the body.

A Survey of Computer Vision-Based Human Motion Capture

A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initial-ization, tracking, pose estimation, and recognition. Each process is discussed and divided into subprocesses and /or categories of methods to provide a reference to describe and compare the more than 130 publications covered by the survey. References are included throughout the paper to exemplify important issues and their relations to the various methods. A number of general assumptions used in this research field are identified and the character of these assumptions indicates that the research field is still in an early stage of development. To evaluate the state of the art, the major application areas are identified and performances are analyzed in light of the methods presented in the survey. Finally, suggestions for future research directions are offered.