PT Unknown
AU Arturo Fuentes
   F. Javier Sanchez
   Thomas Voncina
   Jorge Bernal
TI LAMV: Learning to Predict Where Spectators Look in Live Music Performances
BT 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
PY 2021
BP 500
EP 507
VL 5
DI 10.5220/0010254005000507
AB The advent of artificial intelligence has supposed an evolution on how different daily work tasks are performed. The analysis of cultural content has seen a huge boost by the development of computer-assisted methods that allows easy and transparent data access. In our case, we deal with the automation of the production of live shows, like music concerts, aiming to develop a system that can indicate the producer which camera to show based on what each of them is showing. In this context, we consider that is essential to understand where spectators look and what they are interested in so the computational method can learn from this information. The work that we present here shows the results of a first preliminary study in which we compare areas of interest defined by human beings and those indicated by an automatic system. Our system is based on the extraction of motion textures from dynamic Spatio-Temporal Volumes (STV) and then analyzing the patterns by means of texture analysis techniques. We validate our approach over several video sequences that have been labeled by 16 different experts. Our method is able to match those relevant areas identified by the experts, achieving recall scores higher than 80% when a distance of 80 pixels between method and ground truth is considered. Current performance shows promise when detecting abnormal peaks and movement trends.
ER