Unsupervised grounding of textual descriptions of object features and actions in video

We propose a novel method for learning visual concepts and their correspondence to the words of a natural language. The concepts and correspondences are jointly inferred from video clips depicting simple actions involving multiple objects, together with corresponding natural language commands that would elicit these actions. Individual objects are first detected, together with quantitative measurements of their colour, shape, location and motion. Visual concepts emerge from the co-occurrence of regions within a measurement space and words of the language. The method is evaluated on a set of videos generated automatically using computer graphics from a database of initial and goal configurations of objects. Each video is annotated with multiple commands in natural language obtained from human annotators using crowd sourcing

Qualitative and quantitative spatio-temporal relations in daily living activity recognition

For the effective operation of intelligent assistive systems working in real-world human environments, it is important to be able to recognise human activities and their intentions. In this paper we propose a novel approach to activity recognition from visual data. Our approach is based on qualitative and quantitative spatio-temporal features which encode the interactions between human subjects and objects in an efficient manner. Unlike the state of the art, our approach uses significantly fewer assumptions and does not require knowledge about object types, their affordances, or the sub-level activities that high-level activities consist of. We perform an automatic feature selection process which provides the most representative descriptions of the learnt activities. We validated the method using these descriptions on the CAD-120 benchmark dataset, consisting of video sequences showing humans performing daily real-world activities. The method is shown to outperform state of the art benchmarks

A qualitative approach for online activity recognition

We present a novel qualitative, dynamic length sliding window method which enables a mobile robot to temporally segment activities taking place in live RGB-D video. We demonstrate how activities can be learned from observations by encoding qualitative spatio-temporal relationships between entities in the scene. We also show how a Nearest Neighbour model can recognise activities taking place even if they temporally co-occur. Our system is validated on a challenging dataset of daily living activities

QSRlib: a software library for online acquisition of qualitative spatial relations from video

There is increasing interest in using Qualitative Spatial Relations as a formalism to abstract from noisy and large amounts of video data in order to form high level conceptualisations, e.g. of activities present in video. We present a library to support such work. It is compatible with the Robot Operating System (ROS) but can also be used stand alone. A number of QSRs are built in; others can be easily added

The STRANDS project: long-term autonomy in everyday environments

Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance

Survey of advances and challenges in intelligent autonomy for distributed cyber-physical systems

With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined

Imbalance of demand and supply for regionalized injury services: a case study in Greece

Objective(s). To study regionalized acute injury services on an island with high seasonal fluctuation of the population at risk. Design. An on-going prospective study Setting. The District Hospital in the touristic island of Corfu. Study participants, Of 9432 individuals with traumatic injuries who contacted the hospital during 1996, 1204 were hospitalized. information was recorded on several injury-related clinical and sociodemographic variables. Possible residual disabilities, 6 months after the injury, were also assessed. Main outcome measures. Injury Severity Score (ISS), clinical outcome and duration of hospitalization, odds of transfer to other institutions. Results. Non-residents, whether Greek or foreign nationals are hospitalized for shorter periods. Motor vehicle accident victims are hospitalized on average for 15% longer. Injury victims admitted on a Friday are hospitalized for a longer period. Finally, ISS is a powerful positive predictor of duration of hospitalization. Male injury victims, those injured during late night or early morning and patients injured in July are more likely to be transferred to another institution. Age of the patient and ISS are powerful and independent predictors of an unfavourable outcome. Conclusion. The extra demand created by injured tourists is reflected in the seasonality of admissions for injuries. The district hospital of Kerkyra cannot be considered as deficient in comparison to other district hospitals. Nevertheless, the suboptimal function of the hospital, with respect to injuries, is reflected in the high proportion of injured patients transferred when the injury occurs outside the full working schedule of the hospital. Patients with burns, bone fractures or dislocations and head injuries or concussion are transferred with an overall frequency of about 15% - too high to be compatible with a well functioning secondary care institution