This paper presents a method for rational behaviour recognition that combines vision-based pose estimation with knowledge modeling and reasoning. The proposed method consists of two stages. First, RGB-D images are used in the estimation of the body postures. Then, estimated actions are evaluated to verify that they make sense. This method requires rational behaviour to be exhibited. To comply with this requirement, this work proposes a rational RGB-D dataset with two types of sequences, some for training and some for testing. Preliminary results show the addition of knowledge modeling and reasoning leads to a significant increase of recognition accuracy when compared to a system based only on computer vision.

Extending the services offered by the city requires, most of the times, reimplementation efforts. This paper presents our on-going efforts to develop a platform for the Smart City that focuses in providing the appropriate solutions for an easy integration of new services and devices. This endeavor is accomplished by abstracting communication issues using a middleware platform and by standardizing the way services are instantiating.