Anchor-based group detection in crowd scenes
Mulin Chen, Qi Wang, Xuelong Li
Abstract
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.
BibTeX
@inproceedings{icassp2017_anchorbasedgroup,
title = {Anchor-based group detection in crowd scenes},
author = {Mulin Chen and Qi Wang and Xuelong Li},
booktitle = {ICASSP 2017},
year = {2017}
}