Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

The business of fashion in the nineteenth and twentieth centuries

Fashion is a complex social and cultural phenomenon with strong economic implications. Historical analysis reveals that the mechanisms of creating and spreading fashion have not remained constant, but have varied according to social structures, forms of producing and distributing apparel and social media, while the level of influence of fashion on society has increased in line with economic development. This special issue of Investigaciones de Historia Económica-Economic History Research is dedicated to fashion as an economic phenomenon in the contemporary period. The four articles which make it up show the plurality of the subject areas, sources and methodological approaches in the current research on this topic.