Real-Time Human Motion Capture with Multiple Depth Cameras

Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts without requiring an explicit shape model. The body joint locations are then recovered by combining evidence from multiple views in real-time. We also introduce a dataset of ~6 million synthetic depth frames for pose estimation from multiple cameras and exceed state-of-the-art results on the Berkeley MHAD dataset.Comment: Accepted to computer robot vision 201

Play and Learn: Using Video Games to Train Computer Vision Models

Video games are a compelling source of annotated data as they can readily provide fine-grained groundtruth for diverse tasks. However, it is not clear whether the synthetically generated data has enough resemblance to the real-world images to improve the performance of computer vision models in practice. We present experiments assessing the effectiveness on real-world data of systems trained on synthetic RGB images that are extracted from a video game. We collected over 60000 synthetic samples from a modern video game with similar conditions to the real-world CamVid and Cityscapes datasets. We provide several experiments to demonstrate that the synthetically generated RGB images can be used to improve the performance of deep neural networks on both image segmentation and depth estimation. These results show that a convolutional network trained on synthetic data achieves a similar test error to a network that is trained on real-world data for dense image classification. Furthermore, the synthetically generated RGB images can provide similar or better results compared to the real-world datasets if a simple domain adaptation technique is applied. Our results suggest that collaboration with game developers for an accessible interface to gather data is potentially a fruitful direction for future work in computer vision.Comment: To appear in the British Machine Vision Conference (BMVC), September 2016. -v2: fixed a typo in the reference

Academic Blade Geometries for Baseline Comparisons of Forced Vibration Response Predictions

Predicting the damping associated with underplatform dampers remains a challenge in turbomachinery blade and friction damper design. Turbomachinery blade forced response analysis methods usually rely on nonlinear codes and reduced order models to predict vibration characteristics of blades. Two academic blade geometries coupled with underplatform dampers are presented here for comparison of these model reduction and forced response simulation techniques. The two blades are representative of free-standing turbine blades and exhibit qualitatively similar behavior as highly-complex industrial blades. This thesis fully describes the proposed academic blade geometries and models; it further analyzes and predicts the blades forced response characteristics using the same procedure as industry blades. This analysis classifies the results in terms of resonance frequency, vibration amplitude, and damping over a range of aerodynamic excitation to examine the vibration behavior of the blade/damper system. Additionally, the analysis investigates the effect variations of the contact parameters (friction coefficient, damper / platform roughness and damper mass) have on the predicted blade vibration characteristics, with sensitivities to each parameter. Finally, an investigation of the number of modes retained in the reduced order model shows convergence behavior as well as providing additional data for comparison with alternative model reduction and forced response prediction methods. The academic blade models are shown to behave qualitatively similar to high fidelity industry blade models when the number of retained modes in a modal analysis are varied and behave qualitatively similar under sensitives to design parameters