Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables 3D human pose estimation using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.Comment: 12 pages, Accepted at Eurographics 201

Tracking and modelling motion for biomechanical analysis

This thesis focuses on the problem of determining appropriate skeletal configurations for which a virtual animated character moves to desired positions as smoothly, rapidly, and as accurately as possible. During the last decades, several methods and techniques, sophisticated or heuristic, have been presented to produce smooth and natural solutions to the Inverse Kinematics (IK) problem. However, many of the currently available methods suffer from high computational cost and production of unrealistic poses. In this study, a novel heuristic method, called Forward And Backward Reaching Inverse Kinematics (FABRIK), is proposed, which returns visually natural poses in real-time, equally comparable with highly sophisticated approaches. It is capable of supporting constraints for most of the known joint types and it can be extended to solve problems with multiple end effectors, multiple targets and closed loops. FABRIK was compared against the most popular IK approaches and evaluated in terms of its robustness and performance limitations. This thesis also includes a robust methodology for marker prediction under multiple marker occlusion for extended time periods, in order to drive real-time centre of rotation (CoR) estimations. Inferred information from neighbouring markers has been utilised, assuming that the inter-marker distances remain constant over time. This is the first time where the useful information about the missing markers positions which are partially visible to a single camera is deployed. Experiments demonstrate that the proposed methodology can effectively track the occluded markers with high accuracy, even if the occlusion persists for extended periods of time, recovering in real-time good estimates of the true joint positions. In addition, the predicted positions of the joints were further improved by employing FABRIK to relocate their positions and ensure a fixed bone length over time. Our methodology is tested against some of the most popular methods for marker prediction and the results confirm that our approach outperforms these methods in estimating both marker and CoR positions. Finally, an efficient model for real-time hand tracking and reconstruction that requires a minimum number of available markers, one on each finger, is presented. The proposed hand model is highly constrained with joint rotational and orientational constraints, restricting the fingers and palm movements to an appropriate feasible set. FABRIK is then incorporated to estimate the remaining joint positions and to fit them to the hand model. Physiological constraints, such as inertia, abduction, flexion etc, are also incorporated to correct the final hand posture. A mesh deformation algorithm is then applied to visualise the movements of the underlying hand skeleton for comparison with the true hand poses. The mathematical framework used for describing and implementing the techniques discussed within this thesis is Conformal Geometric Algebra (CGA)

06241 Abstracts Collection -- Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop

From 11.06.06 to 16.06.06, the Dagstuhl Seminar 06241 ``Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop "Theoretical Foundations of Computer Vision"\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

Switching Local and Covariance Matching for Efficient Object Tracking

The covariance tracker finds the targets in consecutive frames by global searching. Covariance tracking has achieved impressive successes thanks to its ability of capturing spatial and statistical properties as well as the correlations between them. Nevertheless, the covariance tracker is relatively inefficient due to its heavy computational cost of model updating and comparing the model with the covariance matrices of the candidate regions. Moreover, it is not good at dealing with articulated object tracking since integral histograms are employed to accelerate the searching process. In this work, we aim to alleviate the computational burden by selecting appropriate tracking approaches. We compute foreground probabilities of pixels and localize the target by local searching when the tracking is in steady states. Covariance tracking is performed when distractions, sudden motions or occlusions are detected. Different from the traditional covariance tracker, we use Log-Euclidean metrics instead of Riemannian invariant metrics which are more computationally expensive. The proposed tracking algorithm has been verified on many video sequences. It proves more efficient than the covariance tracker. It is also effective in dealing with occlusions, which are an obstacle for local mode-seeking trackers such as the mean-shift tracker. 1

Humanoid synthesis using clifford algebra

One of the challenges in the simulation of human motion, either applied to humanoid robots or avatars in virtual environments, is to design a kinematics structure and a set of joint trajectories that move a robot or avatar in a human-like manner. In this paper, a technique is introduced to create accurate humanlike motion with a simplified topology as a reference. Using an optical motion capture system, a finite number of key poses are captured from different subjects performing full body articulated movements. Motion is modeled using the Clifford algebra of dual quaternions and dimensional synthesis techniques are applied to generate the kinematic skeleton of a 3D avatar or robot. The synthesized kinematic skeleton provides location of joints and dimensions of the links forming the limbs, as well as the joint trajectories. Five serial chains constitute our approximation to the human skeleton. Revolute, universal and spherical joints are employed, although other topologies can be used in a similar fashion. Several real datasets are evaluated and results demonstrate that good accuracy can be obtained at interactive rates using the presented methodology. The results show that using simple serial chains in combination with dimensional synthesis suffices to generate the mechanical structure and trajectories of a humanoid robot or 3D avatar mimicking human motion.Postprint (author’s final draft

SEGMENTATION, RECOGNITION, AND ALIGNMENT OF COLLABORATIVE GROUP MOTION

Modeling and recognition of human motion in videos has broad applications in behavioral biometrics, content-based visual data analysis, security and surveillance, as well as designing interactive environments. Significant progress has been made in the past two decades by way of new models, methods, and implementations. In this dissertation, we focus our attention on a relatively less investigated sub-area called collaborative group motion analysis. Collaborative group motions are those that typically involve multiple objects, wherein the motion patterns of individual objects may vary significantly in both space and time, but the collective motion pattern of the ensemble allows characterization in terms of geometry and statistics. Therefore, the motions or activities of an individual object constitute local information. A framework to synthesize all local information into a holistic view, and to explicitly characterize interactions among objects, involves large scale global reasoning, and is of significant complexity. In this dissertation, we first review relevant previous contributions on human motion/activity modeling and recognition, and then propose several approaches to answer a sequence of traditional vision questions including 1) which of the motion elements among all are the ones relevant to a group motion pattern of interest (Segmentation); 2) what is the underlying motion pattern (Recognition); and 3) how two motion ensembles are similar and how we can 'optimally' transform one to match the other (Alignment). Our primary practical scenario is American football play, where the corresponding problems are 1) who are offensive players; 2) what are the offensive strategy they are using; and 3) whether two plays are using the same strategy and how we can remove the spatio-temporal misalignment between them due to internal or external factors. The proposed approaches discard traditional modeling paradigm but explore either concise descriptors, hierarchies, stochastic mechanism, or compact generative model to achieve both effectiveness and efficiency. In particular, the intrinsic geometry of the spaces of the involved features/descriptors/quantities is exploited and statistical tools are established on these nonlinear manifolds. These initial attempts have identified new challenging problems in complex motion analysis, as well as in more general tasks in video dynamics. The insights gained from nonlinear geometric modeling and analysis in this dissertation may hopefully be useful toward a broader class of computer vision applications