@inproceedings{8c3f007c0b6941e98c382a33cd1ae056,
title = "Anchor-based group detection in crowd scenes",
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.",
keywords = "Clustering, Crowd Motion, Group Detection, Manifold Structure",
author = "Mulin Chen and Qi Wang and Xuelong Li",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952382",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1378--1382",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}