Group activity description and recognition based on trajectory analysis and neural networks
Jorge Azorín-López, Marcelo Saval-Calvo, Andrés Fuster-Guilló, José García‐Rodríguez, Miguel Cazorla, María Teresa Signes Pont · 2016
The recognition of group activities using computer vision and pattern recognition methods has been, and still remains, a challenging problem. Most of the research on human behaviour has been focused on recognizing individual issues from actions to behaviours. However, the analysis and recognition of group activities, the relationships of different groups in the scene and the interaction of the individuals in the group is still considered an open problem. This paper proposes a novel representation method to analyse and recognise group activities, called Group Activity Descriptor Vector (GADV). It is calculated from the trajectory described by the group and by the individuals who form it. Specifically, the GADV describes three different components: the trajectory followed by the group, the coherence of the individual trajectories in the group and, finally, the movement relationships among different groups in the scene. The trajectory analysis allows a simple high level understanding of complex groups activities. The GADV representation has been evaluated with different self-organizing neural networks using Behave and Caviar dataset sequences obtaining great accuracy in the recognition of the group activities, outperforming the state of the art methods.