A Multi-camera Network System for Markerless 3D Human Body Voxel Reconstruction

This paper presents a fully automated system for real-time 3D human visual hull reconstruction and skeleton vox-els extraction. The main contributions include: (1) A novel network based system is presented, which uses AXIS net-work cameras as video capture device, and performs a parallel processing among data capture, 3D voxel recon-struction and display. (2) A new human visual hull re-construction algorithm is given. This approach firstly seg-ments the foreground accurately by an efficient Gaussian Mixture Model (GMM) and a shadow model in HSV color space, then extends the standard Shape-From-Silhouette (SFS) algorithm with online Region-of-Interest (ROI) esti-mation and binary searching, and finally construct skele-ton probability visual hull with distance transform. Exper-iments with real video sequences show that the system can process eleven 640x480 video sequences at a frame rate of 15fps, and construct human body voxels reliably in complex scenarios with cast shadows, various body configurations and multiple persons. 1

Tracking object poses in the context of robust body pose estimates

This work focuses on tracking objects being used by humans. These objects are often small, fast moving and heavily occluded by the user. Attempting to recover their 3D position and orientation over time is a challenging research problem. To make progress we appeal to the fact that these objects are often used in a consistent way. The body poses of different people using the same object tend to have similarities, and, when considered relative to those body poses, so do the respective object poses. Our intuition is that, in the context of recent advances in body-pose tracking from RGB-D data, robust object-pose tracking during human-object interactions should also be possible. We propose a combined generative and discriminative tracking framework able to follow gradual changes in object-pose over time but also able to re-initialise object-pose upon recognising distinctive body-poses. The framework is able to predict object-pose relative to a set of independent coordinate systems, each one centred upon a different part of the body. We conduct a quantitative investigation into which body parts serve as the best predictors of object-pose over the course of different interactions. We find that while object-translation should be predicted from nearby body parts, object-rotation can be more robustly predicted by using a much wider range of body parts. Our main contribution is to provide the first object-tracking system able to estimate 3D translation and orientation from RGB-D observations of human-object interactions. By tracking precise changes in object-pose, our method opens up the possibility of more detailed computational reasoning about human-object interactions and their outcomes. For example, in assistive living systems that go beyond just recognising the actions and objects involved in everyday tasks such as sweeping or drinking, to reasoning that a person has missed sweeping under the chair or not drunk enough water today. © 2014 Elsevier B.V. All rights reserved

Enhanced facial expression using oxygenation absorption of facial skin

Facial skin appearance is affected by physical and physiological state of the skin. The facial expression especially the skin appearances are in constant mutability and dynamically changed as human behave, talk and stress. The color of skin is considered to be one of the key indicators for these symptoms. The skin color resolution is highly determined by the scattering and absorption of light within the skin layers. The concentration of chromophores in melanin and hemoglobin oxygenation in the blood plays a pivotal role. An improvement work on prior model to create a realistic textured three-dimensional (3D) facial model for animation is proposed. This thesis considers both surface and subsurface scattering capable of simulating the interaction of light with the human skin. Furthermore, six parameters are used in this research which are the amount of oxygenation, de-oxygenation, hemoglobin, melanin, oil and blend factor for different types of melanin in the skin to generate a perfect match to specific skin types. The proposed model is associated with Blend Shape Interpolation and Facial Action Coding System to create five basic facial emotional expressions namely anger, happy, neutral, sad and fear. Meanwhile, the correlation between blood oxygenation in changing facial skin color for basic natural emotional expressions are measured using the Pulse Oximetry and 3D skin analyzer. The data from different subjects with male and female under different number of partially extreme facial expressions are fed in the model for simulation. The multi-pole method for layered materials is used to calculate the spectral diffusion profiles of two-layered skin which are further utilized to simulate the subsurface scattering of light within the skin. While the subsurface scattering is further combined with the Torrance-Sparrow Bidirectional Reflectance Distribution Function (BRDF) model to simulate the interaction of light with an oily layer at the skin surface. The result is validated by an evaluation procedure for measuring the accountability of a facial model via expressions and skin color of proposed model to the real human. The facial expressions evaluation is verified by calculating Euclidean distance between the facial markers of the real human and the avatar. The second assessment validates the skin color of facial expressions for the proposed avatar via the extraction of Histogram Color Features and Color Coherence Vector of each image with the real human and the previous work. The experimental result shows around 5.12 percent improvement compared to previous work. In achieving the realistic facial expression for virtual human based on facial skin color, texture and oxygenation of hemoglobin, the result demonstrates that the proposed model is beneficial to the development of virtual reality and game environment of computer aided graphics animation systems

Kinematic State Estimation using Multiple DGPS/MEMS-IMU Sensors

Animals have evolved over billions of years and understanding these complex and intertwined systems have potential to advance the technology in the field of sports science, robotics and more. As such, a gait analysis using Motion Capture (MOCAP) technology is the subject of a number of research and development projects aimed at obtaining quantitative measurements. Existing MOCAP technology has limited the majority of studies to the analysis of the steady-state locomotion in a controlled (indoor) laboratory environment. MOCAP systems such as the optical, non-optical acoustic and non-optical magnetic MOCAP systems require predefined capture volume and controlled environmental conditions whilst the non-optical mechanical MOCAP system impedes the motion of the subject. Although the non-optical inertial MOCAP system allows MOCAP in an outdoor environment, it suffers from measurement noise and drift and lacks global trajectory information. The accuracy of these MOCAP systems are known to decrease during the tracking of the transient locomotion. Quantifying the manoeuvrability of animals in their natural habitat to answer the question “Why are animals so manoeuvrable?” remains a challenge. This research aims to develop an outdoor MOCAP system that will allow tracking of the steady-state as well as the transient locomotion of an animal in its natural habitat outside a controlled laboratory condition. A number of researchers have developed novel MOCAP systems with the same aim of creating an outdoor MOCAP system that is aimed at tracking the motion outside a controlled laboratory (indoor) environment with unlimited capture volume. These novel MOCAP systems are either not validated against the commercial MOCAP systems or do not have comparable sub-millimetre accuracy as the commercial MOCAP systems. The developed DGPS/MEMS-IMU multi-receiver fusion MOCAP system was assessed to have global trajectory accuracy of _0:0394m, relative limb position accuracy of _0:006497m. To conclude the research, several recommendations are made to improve the developed MOCAP system and to prepare for a field-testing with a wild animal from a family of a terrestrial megafauna

Human Motion Analysis: From Gait Modeling to Shape Representation and Pose Estimation

This dissertation presents a series of fundamental approaches to the human motion analysis from three perspectives, i.e., manifold learning-based gait motion modeling, articulated shape representation and efficient pose estimation. Firstly, a new joint gait-pose manifold (JGPM) learning algorithm is proposed to jointly optimize the gait and pose variables simultaneously. To enhance the representability and flexibility for complex motion modeling, we also propose a multi-layer JGPM that is capable of dealing with a variety of walking styles and various strides. We resort to a topologically-constrained Gaussian process latent variable model (GPLVM) to learn the multi-layer JGPM where two new techniques are introduced to facilitate model learning. First is training data diversification that creates a set of simulated motion data with different strides under limited data. Second is the topology-aware local learning that is to speed up model learning by taking advantage of the local topological structure. We demonstrate the effectiveness of our approach by synthesizing the high-quality motions from the multi-layer model. The experimental results show that the multi-layer JGPM outperforms several existing GPLVM-based models in terms of the overall performance of motion modeling.On the other hand, to achieve efficient human pose estimation from a single depth sensor, we develop a generalized Gaussian kernel correlation (GKC)-based framework which supports not only body shape modeling, but also articulated pose tracking. We first generalize GKC from the univariate Gaussian to the multivariate one and derive a unified GKC function that provides a continuous and differentiable similarity measure between a template and an observation, both of which are represented by a collection of univariate and/or multivariate Gaussian kernels. Then, to facilitate the data matching and accommodate articulated body deformation, we embed a quaternion-based articulated skeleton into a collection of multivariate Gaussians-based template model and develop an articulated GKC (AGKC) which supports subject-specific shape modeling and articulated pose tracking for both the full-body and hand. Our tracking algorithm is simple yet effective and computationally efficient. We evaluate our algorithm on two benchmark depth datasets. The experimental results are promising and competitive when compared with state-of-the-art algorithms.Electrical Engineerin