In this paper, we present an approach to detect, track people, and recognize poses. The detected poses are used for controlling a real time spatial game. In the people detection, tracking and pose recognition system, body parts such as the torso and the hands are segmented from the whole body and tracked over time. The 2D coordinates of these body parts are used as the input of a pose recogniti...

In this paper, we propose a method for simultaneous human full-body pose tracking and activity recognition from time-of-flight (ToF) camera images. Simple and sparse depth cues are used together with a prior motion model that constrains the tracking problem. Our model consists of low-dimensional manifolds of feasible poses for multiple activities. A particle filter allows us to efficiently eval...

In this paper, we introduce a framework for carrying object detection in different people from different views using pose preserving dynamic shape models. We model dynamic shape deformations in different people using kinematics manifold embedding and decomposable generative models by kernel map and multilinear analysis. The generative model supports pose-preserving shape reconstruction in diffe...

We present an augmented reality interface with markerless human body interaction. It consists of 3D motion capture of the human body and the processing of 3D human poses for augmented reality applications. A monocular camera is used to acquire the images of the user’s motion for 3D pose estimation. In the proposed technique, a graphical 3D human model is first constructed. Its projection on a v...

For more than a century, rigid-body displacements have been viewed as affine transformations described as homogeneous transformation matrices wherein the linear part is a rotation matrix. In group-theoretic terms, this classical description makes rigid-body motions a semi-direct product. The distinction between a rigid-body displacement of Euclidean space and a change in pose from one reference...

In this paper I propose a computer vision-based method for analyzing the human pose during physical exercises performed for a virtual trainer. Body part labeling and difference analysis between a measurement and reference silhouette are combined into a result consisting of a set of vectors per body part, indicating the errors made. The approach depends heavily on a statistical model of the huma...

Reliable, efficient human pose estimation from images is a precursor to many useful applications including advanced human computer interfaces, surveillance systems, image archive analysis and smart environments. Whilst progress has been made on human pose estimation, the research often makes strong assumptions about the appearance. In particular, assumptions are often made regarding the backgro...

In this paper, a method is presented that allows reconstructing the full-body pose of a person in real-time, based on the limited input from a few wearable inertial sensors. Our method uses Gaussian Process Regression to learn the person-specific functional relationship between the sensor measurements and full-body pose. We generate training data by recording sample movements for different acti...

This paper presents the upper human body pose modelling using windowing and template matching techniques. Indoor image sequences are used as input. Silhouette and colour features are extracted in the human body parts detection stage. The head and torso pose are estimated using windowing technique. Meanwhile the upper and lower arms are estimated using template matching technique. Each body part...

This paper proposes a pipeline for lying pose recognition from single images, which is designed for health-care robots to find fallen people. We firstly detect object bounding boxes by a mixture of viewpoint-specific part based model detectors and later estimate a detailed configuration of body parts on the detected regions by a finer tree-structured model. Moreover, we exploit the information ...