Intelligent surveillance refers to using Artificial Intelligence techniques in order to improve surveillance and deal with semantic information obtained from low-level security devices. In this context, the use of expert knowledge may offer a more realistic solution when dealing with the design of a surveillance system. In this work, a conceptual framework based on normality analysis to detect abnormal behaviors by means of normality concepts is presented. A normality concept specifies how a certain object should ideally behave in a concrete environment depending on such a concept. The definition of the normal path concept is studied in depth in order to analyze behaviors in an outdoor environment.

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This paper presents a system for detecting road departures by comparing linguistic representations for the trajectory of the vehicle with that for the lane marks of the road. All this information is obtained from a single camera processing exclusively the H264 motion vectors extracted from the recorded video. The process of comparison between the linguistic elements allows detecting the subset of continuous frames where there is no logical correspondence between the displacement of the vehicle and the road shape. Since the videos are captured from a moving vehicle, we propose a statistically based process to use domain changing fuzzy sets adapted to traffic scenarios that continuously change. This improves the reliability of the linguistic descriptions that, once compared, are used to detect departures. Lastly, a set of experiments using traffic videos with different characteristics are presented to validate this approach.

Thanks to the presence of sensors and the boom in technologies typical of the Internet of things, we can now monitor and record the energy consumption of buildings over time. By effectively analyzing these data to capture consumption patterns, significant reductions in consumption can be achieved and this can contribute to a building's sustainability. In this work, we propose a framework from which we can define models that capture this casuistry, gathering a set of time series of electrical consumption. The objective of these models is to obtain a linguistic summary based on y is P protoforms that describes in natural language the consumption of a given building or group of buildings in a specific time period. The definition of these descriptions has been solved by means of fuzzy linguistic summaries. As a novelty in this field, we propose an extension that is able to capture situations where the membership of the fuzzy sets is not very marked, which obtains an enriched semantics. In addition, to support these models, the development of a software prototype has been carried out and a small applied study of actual consumption data from an educational organization based on the conclusions that can be drawn from the techniques that we have described, demonstrating its capabilities in summarizing consumption situations. Finally, it is intended that this work will be useful to managers of buildings or organizational managers because it will enable them to better understand consumptionin a brief and concise manner, allowing them to save costs derived from energy supply by establishing sustainable policies.

Rendering is the process of generating a 2D image from the abstract description of a 3D scene. In spite of the development of new techniques and algorithms, the computational requirements of photorealistic rendering are huge so that it is not possible to render them in real time. In addition, the adequate configuration of rendering quality parameters is very difficult to be done by inexpert users, and they are usually set higher than in fact are needed. This article presents an architecture called MAgArRO to optimize the rendering process in a distributed, noncentralized way through a multiagent solution, by making use of expert knowledge or previous jobs to reduce the final rendering. Experimental results prove that this novel approach offers a promising research line to optimize the rendering of photorealistic images.