The problem of rigid body pose estimation is treated in discrete-time via discrete Lagrange-d'Alembert principle and Lyapunov methods. position attitude the are to be estimated simultaneously with help vision inertial sensors. For pose, continuous-time kinematics equations discretized appropriately. We approach as minimising energies stored errors quantities. With measurements obtained through ...

We consider the problem of monocular 3d body pose tracking from video sequences. This task is inherently ambiguous. We propose to learn a generative model of the relationship of body pose and image appearance using a sparse kernel regressor. Within a particle filtering framework, the potentially multimodal posterior probability distributions can then be inferred. The 2d bounding box location of...

Abstract Accurate and temporally consistent modeling of human bodies is essential for a wide range applications, including character animation, understanding social behavior, AR/VR interfaces. Capturing motion accurately from monocular image sequence remains challenging; quality strongly influenced by temporal consistency the captured body motion. Our work presents an elegant solution to integr...

In this paper, we represent human actions as short sequences of atomic body poses. The knowledge of body pose is stored only implicitly as a set of silhouettes seen from multiple viewpoints; no explicit 3D poses or body models are used, and individual body parts are not identified. Actions and their constituent atomic poses are extracted from a set of multiview multiperson video sequences by an...

Given a image photographed somebody in action, we describe a dual-generative-model approach for estimating human body pose from silhouette. In contrast to existing techniques, which mostly learn regression model whereby make inference of body pose for unknown input [1, 2, 6, 7], we transform the problem into searching of the best pair of upper pose and lower pose. This searching strategy can re...

We estimate 2D human pose from video using only optical flow. The key insight is that dense optical flow can provide information about 2D body pose. Like range data, flow is largely invariant to appearance but unlike depth it can be directly computed from monocular video. We demonstrate that body parts can be detected from dense flow using the same random forest approach used by the Microsoft K...

A kinematical model of the ski jumper's body pose at the beginning of take-off was proposed. A method of measuring skier's body coordinates based on the results of video recordings and office information technologies was created. Kinematical parameters of the skier's body pose at the beginning of take-off were determined using sport competition results of 33 ski jumpers. Five parameters of the ...

We propose a method for human full-body pose tracking from measurements of wearable inertial sensors. Since the data provided by such sensors is sparse, noisy and often ambiguous, we use a compound prior model of feasible human poses to constrain the tracking problem. Our model consists of several low-dimensional, activity-specific motion models and an efficient, sampling-based activity switchi...

In this paper, we describe the GPU implementation of a markerless full-body articulated human motion tracking system from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multidimensional nonlinear optimisation problem solved using particle swarm optimisation (PSO). We model the human body pose with a skeleton-driven subdivisionsurface human body mode...