Replication Data for: Automatic Collective Behaviour Recognition

Collective behaviour such as the flocks of birds and schools of fish is inspired by computer-based systems and is widely used in agents’ formation. The human could easily recognise these behaviours; however, it is hard for a computer system to recognise these behaviours. Since humans could easily recognise these behaviours, ground truth data on human perception of collective behaviour could enable machine learning methods to mimic this human perception. Hence ground truth data has been collected from human perception of collective behaviour recognition by running an online survey. Specific collective motions considered in this online survey include 16 structured and unstructured behaviours. The defined structured collective motions include boids’ movements with an identifiable embedded pattern. Unstructured collective motions consist of random movement of boids with no patterns. The participants are from diverse levels of knowledge, all over the world, and are over 18 years old. Each question contains a short video (around 10 seconds), captured from one of the 16 simulated movements. The videos are shown in a randomized order to the participants. Then they were asked to label each structured motion of boids as ‘flocking’, ‘aligned’, or ‘grouped’ and others as ‘not flocking’, ‘not aligned’, or ‘not grouped’. By averaging human perceptions, three binary labelled datasets of these motions are created. The data could be trained by machine learning methods, which enabled them to automatically recognise collective behaviour.