The evolution of mass tourism destinations: new approaches beyond deterministic models in Benidorm (Spain)

Although deterministic models of the evolution of mass tourism coastal resorts predict an almost inevitable decline over time, theoretical frameworks of the evolution and restructuring policies of mature destinations should be revised to reflect the complex and dynamic way in which these destinations evolve and interact with the tourism market and global socio-economic environment. The present study examines Benidorm because its urban and tourism model and large-scale tourism supply and demand make it one of the most unique destinations on the Mediterranean coast. The investigation reveals the need to adopt theories and models that are not purely deterministic. The dialectic interplay between external factors and the internal factors inherent in this destination simultaneously reveals a complex and diverse stage of maturity and the ability of destinations to create their own future.

Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.