Human robot interaction in a crowded environment

Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3]. Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person. Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Visual Servoing

The goal of this book is to introduce the visional application by excellent researchers in the world currently and offer the knowledge that can also be applied to another field widely. This book collects the main studies about machine vision currently in the world, and has a powerful persuasion in the applications employed in the machine vision. The contents, which demonstrate that the machine vision theory, are realized in different field. For the beginner, it is easy to understand the development in the vision servoing. For engineer, professor and researcher, they can study and learn the chapters, and then employ another application method

Patterns and Pattern Languages for Mobile Augmented Reality

Mixed Reality is a relatively new field in computer science which uses technology as a medium to provide modified or enhanced views of reality or to virtually generate a new reality. Augmented Reality is a branch of Mixed Reality which blends the real-world as viewed through a computer interface with virtual objects generated by a computer. The 21st century commodification of mobile devices with multi-core Central Processing Units, Graphics Processing Units, high definition displays and multiple sensors controlled by capable Operating Systems such as Android and iOS means that Mobile Augmented Reality applications have become increasingly feasible. Mobile Augmented Reality is a multi-disciplinary field requiring a synthesis of many technologies such as computer graphics, computer vision, machine learning and mobile device programming while also requiring theoretical knowledge of diverse fields such as Linear Algebra, Projective and Differential Geometry, Probability and Optimisation. This multi-disciplinary nature has led to a fragmentation of knowledge into various specialisations, making it difficult to integrate different solution components into a coherent architecture. Software design patterns provide a solution space of tried and tested best practices for a specified problem within a given context. The solution space is non-prescriptive and is described in terms of relationships between roles that can be assigned to software components. Architectural patterns are used to specify high level designs of complete systems, as opposed to domain or tactical level patterns that address specific lower level problem areas. Pattern Languages comprise multiple software patterns combining in multiple possible sequences to form a language with the individual patterns forming the language vocabulary while the valid sequences through the patterns define the grammar. Pattern Languages provide flexible generalised solutions within a particular domain that can be customised to solve problems of differing characteristics and levels of iii complexity within the domain. The specification of one or more Pattern Languages tailored to the Mobile Augmented Reality domain can therefore provide a generalised guide for the design and architecture of Mobile Augmented Reality applications from an architectural level down to the ”nuts-and-bolts” implementation level. While there is a large body of research into the technical specialisations pertaining to Mobile Augmented Reality, there is a dearth of up-to-date literature covering Mobile Augmented Reality design. This thesis fills this vacuum by: 1. Providing architectural patterns that provide the spine on which the design of Mobile Augmented Reality artefacts can be based; 2. Documenting existing patterns within the context of Mobile Augmented Reality; 3. Identifying new patterns specific to Mobile Augmented Reality; and 4. Combining the patterns into Pattern Languages for Detection & Tracking, Rendering & Interaction and Data Access for Mobile Augmented Reality. The resulting Pattern Languages support design at multiple levels of complexity from an object-oriented framework down to specific one-off Augmented Reality applications. The practical contribution of this thesis is the specification of architectural patterns and Pattern Language that provide a unified design approach for both the overall architecture and the detailed design of Mobile Augmented Reality artefacts. The theoretical contribution is a design theory for Mobile Augmented Reality gleaned from the extraction of patterns and creation of a pattern language or languages