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.诸如鸟群、鱼群的集群行为，其相关研究灵感源自计算机系统，并被广泛应用于智能体编队领域。人类可轻松识别此类集群行为，但计算机系统却难以完成这一任务。鉴于人类可轻松识别此类集群行为，基于人类对集群行为感知所构建的真值标注（ground truth）数据集，能够助力机器学习方法复刻人类的识别感知能力。因此，本研究通过开展线上调研，收集了基于人类对集群行为识别感知的真值标注数据集。本次线上调研涵盖的特定集群运动共16种，分为结构化与非结构化两类。本次定义的结构化集群运动，包含带有可识别内嵌模式的博伊德群体（boids）运动；非结构化集群运动则由无任何模式的博伊德群体随机运动构成。调研参与者来自全球各地，知识水平层次各异，且均年满18周岁。每份调研问题均包含一段时长约10秒的短视频，素材取自16种模拟运动中的一种，且向参与者展示的视频顺序均为随机打乱。随后要求参与者将博伊德群体的各结构化运动分别标注为‘聚群（flocking）’、‘对齐（aligned）’或‘聚合（grouped）’，其余运动则标注为‘非聚群（not flocking）’、‘非对齐（not aligned）’或‘非聚合（not grouped）’。通过对人类感知结果取平均，本研究构建了针对上述运动的三类二分类标注数据集。该数据集可用于机器学习模型的训练，使其能够自动识别集群行为。