group detection aims to classify pedestrians into categories according to their motion dynamics. it's fundamental for analyzing crowd behaviors and involves a wide range of applications. in this paper, we propose a anchor-based manifold ranking (amr) method to detect groups in crowd scenes. our main contributions are threefold: (1) the topological relationship of individuals are effectively investigated with a manifold ranking method; (2) global consistency in crowds are accurately recognized by a coherent merging strategy; (3) the number of groups is decided automatically based on the similarity graph of individuals. experimental results show that the proposed framework is competitive against the state-of-the-art methods. © 2017 ieee.