Intelligent Robotics and Communication Laboratories, Advanced Telecommunications Research Institute International, Kyoto 619-0288, Japan.
Sensors (Basel). 2013 Jan 14;13(1):875-97. doi: 10.3390/s130100875.
Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation.
将属性与人群中的行人相关联对于各种领域都是相关的，例如监控、客户分析和服务提供。感兴趣的属性在很大程度上取决于应用领域，可能涉及朋友或家人等社会关系，以及包括领导者或下属在内的群体层次结构。然而，复杂的社会环境使这项任务变得复杂。我们通过利用人群中的小群体结构来解决这个问题。个体之间的关系及其在社会群体中的同伴关系是社会属性的可靠指标。为此，本文基于通过假设检验方案集成的显式运动模型来识别社会群体。我们开发了两种与位置和方向关系相关的模型。通过并行使用这两个模型，可以确定一对行人是否属于同一组，这定义了一个复合假设检验方案。通过在具有不同环境特性和群体特征的三个数据集上测试所提出的方法，证明我们的识别准确率达到了 87%到 99%。本研究的贡献在于其定义了位置和方向关系模型，描述了复合评估，并通过基于群体关系的本地和全局指标的提出的不确定性度量解决了歧义。
