Fast upper body pose estimation for human-robot interaction

This work describes an upper body pose tracker that finds a 3D pose estimate using video sequences obtained from a monocular camera, with applications in human-robot interaction in mind. A novel mixture of Ornstein-Uhlenbeck processes model, trained in a reduced dimensional subspace and designed for analytical tractability, is introduced. This model acts as a collection of mean-reverting random walks that pull towards more commonly observed poses. Pose tracking using this model can be Rao-Blackwellised, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. The model is used within a recursive Bayesian framework to provide reliable estimates of upper body pose when only a subset of body joints can be detected. Model training data can be extended through a retargeting process, and better pose coverage obtained through the use of Poisson disk sampling in the model training stage. Results on a number of test datasets show that the proposed approach provides pose estimation accuracy comparable with the state of the art in real time (30 fps) and can be extended to the multiple user case. As a motivating example, this work also introduces a pantomimic gesture recognition interface. Traditional approaches to gesture recognition for robot control make use of predefined codebooks of gestures, which are mapped directly to the robot behaviours they are intended to elicit. These gesture codewords are typically recognised using algorithms trained on multiple recordings of people performing the predefined gestures. Obtaining these recordings can be expensive and time consuming, and the codebook of gestures may not be particularly intuitive. This thesis presents arguments that pantomimic gestures, which mimic the intended robot behaviours directly, are potentially more intuitive, and proposes a transfer learning approach to recognition, where human hand gestures are mapped to recordings of robot behaviour by extracting temporal and spatial features that are inherently present in both pantomimed actions and robot behaviours. A Bayesian bias compensation scheme is introduced to compensate for potential classification bias in features. Results from a quadrotor behaviour selection problem show that good classification accuracy can be obtained when human hand gestures are recognised using behaviour recordings, and that classification using these behaviour recordings is more robust than using human hand recordings when users are allowed complete freedom over their choice of input gestures

Biometric Data Art: Personalized Narratives and Multimodal Interaction

Biometric technology has brought enhancements to identification and access control. As more digital applications request people to input their biometric data as a more convenient and secure method of identification, the possibility of losing their personal data and identities may increase. The phenomenon of biometric data abuse causes one to question what their true identity may be and what methods can be used to define identity and hidden narratives. The questions of identification and the insecurity of biometric data have become my inspiration, providing artistic approaches to the manipulation of biometric data and having the potential to suggest new directions for solving the problems. To do so, in-depth investigation of the narratives beyond the visual features of the biometric data is necessary. This content can create a close link between an artwork and its audience by causing the latter to become deeply engaged with the artwork through their own stories.This dissertation examines narratives and artistic explorations discovered from one form of biometric data, fingerprints, drawing on insights from various fields such as genetics, hand analysis, and biology. It also presents contributions on new ways of creating interactive media artworks using fingerprint data based on visual feature analysis of the data and multimodal interaction to explore their sonic signatures. Therefore, the artwork enriches interactive media art by incorporating personalization into the artistic experience, and creates unique personalized experience for each audience member. This thesis documents developments and productions of a series of artworks, Digiti Sonus, by focusing on its conceptual approaches, design, techniques, challenges and future directions

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested