Articulated motion and deformable objects

This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field

Analysis of the hands in egocentric vision: A survey

Egocentric vision (a.k.a. first-person vision - FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or parts of them?); interpretation (what are the hands doing?); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided

Compact Environment Modelling from Unconstrained Camera Platforms

Mobile robotic systems need to perceive their surroundings in order to act independently. In this work a perception framework is developed which interprets the data of a binocular camera in order to transform it into a compact, expressive model of the environment. This model enables a mobile system to move in a targeted way and interact with its surroundings. It is shown how the developed methods also provide a solid basis for technical assistive aids for visually impaired people

Three-dimensional scene recovery for measuring sighting distances of rail track assets from monocular forward facing videos

Rail track asset sighting distance must be checked regularly to ensure the continued and safe operation of rolling stock. Methods currently used to check asset line-of-sight involve manual labour or laser systems. Video cameras and computer vision techniques provide one possible route for cheaper, automated systems. Three categories of computer vision method are identified for possible application: two-dimensional object recognition, two-dimensional object tracking and three-dimensional scene recovery. However, presented experimentation shows recognition and tracking methods produce less accurate asset line-of-sight results for increasing asset-camera distance. Regarding three-dimensional scene recovery, evidence is presented suggesting a relationship between image feature and recovered scene information. A novel framework which learns these relationships is proposed. Learnt relationships from recovered image features probabilistically limit the search space of future features, improving efficiency. This framework is applied to several scene recovery methods and is shown (on average) to decrease computation by two-thirds for a possible, small decrease in accuracy of recovered scenes. Asset line-of-sight results computed from recovered three-dimensional terrain data are shown to be more accurate than two-dimensional methods, not effected by increasing asset-camera distance. Finally, the analysis of terrain in terms of effect on asset line-of-sight is considered. Terrain elements, segmented using semantic information, are ranked with a metric combining a minimum line-of-sight blocking distance and the growth required to achieve this minimum distance. Since this ranking measure is relative, it is shown how an approximation of the terrain data can be applied, decreasing computation time. Further efficiency increases are found by decomposing the problem into a set of two-dimensional problems and applying binary search techniques. The combination of the research elements presented in this thesis provide efficient methods for automatically analysing asset line-of-sight and the impact of the surrounding terrain, from captured monocular video.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Efficient hand orientation and pose estimation for uncalibrated cameras

We proposed a staged probabilistic regression method that is capable of learning well from a number of variations within a dataset. The proposed method is based on multi layered Random Forest, where the first layer consisted of a single marginalization weights regressor and second layer contained an ensemble of expert learners. The expert learners are trained in stages, where each stage involved training and adding an expert learner to the intermediate model. After every stage, the intermediate model was evaluated to reveal a latent variable space defining a subset that the model had difficulty in learning from. This subset was used to train the next expert regressor. The posterior probabilities for each training sample were extracted from each expert regressors. These posterior probabilities were then used along with a Kullback-Leibler divergence-based optimization method to estimate the marginalization weights for each regressor. A marginalization weights regressor was trained using CDF and the estimated marginalization weights. We showed the extension of our work for simultaneous hand orientation and pose inference. The proposed method outperformed the state-of-the-art for marginalization of multi-layered Random Forest and hand orientation inference. Furthermore, we show that a method which simultaneously learns from hand orientation and pose outperforms pose classification as it is able to better understand the variations in pose induced due to viewpoint changes