The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

Context-Aware Personalized Activity Modeling in Concurrent Environment

Activity recognition, having endemic impact on smart homes, faces one of the biggest challenges in learning a personalized activity model completely by using a generic model especially for parallel and interleaved activities. Furthermore, inhabitant’s mistaken object interaction may entail in another spurious activity at smart homes. Identifying and removing such spurious activities is another challenging task. Knowledge driven techniques used for recognizing activity models are static in nature, lack contextual representation and may not comprehend spurious actions for parallel/interleaved activities. In this paper, a novel approach for completing the personalized model specific to each inhabitant at smart homes using generic model (incomplete) is presented that can recognize the sequential, parallel, and interleaved activities dynamically while removing the spurious activities semantically. A comprehensive set of experiments and results based upon number of correct (true positivity) or incorrect (false negativity) recognition of activities assert effectiveness of presented approach within a smart hom

LifeLogging: personal big data

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self

On Inter-referential Awareness in Collaborative Augmented Reality

For successful collaboration to occur, a workspace must support inter-referential awareness - or the ability for one participant to refer to a set of artifacts in the environment, and for that reference to be correctly interpreted by others. While referring to objects in our everyday environment is a straight-forward task, the non-tangible nature of digital artifacts presents us with new interaction challenges. Augmented reality (AR) is inextricably linked to the physical world, and it is natural to believe that the re-integration of physical artifacts into the workspace makes referencing tasks easier; however, we find that these environments combine the referencing challenges from several computing disciplines, which compound across scenarios. This dissertation presents our studies of this form of awareness in collaborative AR environments. It stems from our research in developing mixed reality environments for molecular modeling, where we explored spatial and multi-modal referencing techniques. To encapsulate the myriad of factors found in collaborative AR, we present a generic, theoretical framework and apply it to analyze this domain. Because referencing is a very human-centric activity, we present the results of an exploratory study which examines the behaviors of participants and how they generate references to physical and virtual content in co-located and remote scenarios; we found that participants refer to content using physical and virtual techniques, and that shared video is highly effective in disambiguating references in remote environments. By implementing user feedback from this study, a follow-up study explores how the environment can passively support referencing, where we discovered the role that virtual referencing plays during collaboration. A third study was conducted in order to better understand the effectiveness of giving and interpreting references using a virtual pointer; the results suggest the need for participants to be parallel with the arrow vector (strengthening the argument for shared viewpoints), as well as the importance of shadows in non-stereoscopic environments. Our contributions include a framework for analyzing the domain of inter-referential awareness, the development of novel referencing techniques, the presentation and analysis of our findings from multiple user studies, and a set of guidelines to help designers support this form of awareness