There has been an increasing interest on the analysis of First Person Videos in the last few years due to the spread of low-cost wearable devices. Nevertheless, the understanding of the environment surrounding the wearer is a difficult task with many elements involved. In this work, a method for detecting and mapping the presence of people and crowds around the wearer is presented. Features extracted at the crowd level are used for building a robust representation that can handle the variations and occlusion of people’s visual characteristics inside a crowd. To this aim, convolutional neural networks have been exploited. Results demonstrate that this approach achieves a high accuracy on the recognition of crowds, as well as the possibility of a general interpretation of the context trough the classification of characteristics of the segmented background.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
|Titolo:||Convolutional Neural Networks for Detecting and Mapping Crowds in First Person Vision Applications|
|Data di pubblicazione:||2015|
|Appare nelle tipologie:||04.01 - Contributo in atti di convegno|