@inproceedings{6234bdbd5a144927a01ce06129f91f7f,
title = "A Multi-View Pedestrian Tracking Framework Based on Graph Matching",
abstract = "In the applications of video monitoring over large public or private spaces, multiple cameras are required to cover the entire space and resolve the problems of occlusion, object intersection and so on. In this work, a novel multi-view pedestrian tracking framework is proposed to simultaneously detect and associate human objects across views using graph matching techniques to fully exploit the object features and the spatial/temporal relationships among the objects. Experimental results are provided to demonstrate the accuracy of our proposed framework.",
keywords = "Graph Matching, Multiview Tracking, Object Association, Pedestrian Detection",
author = "Fanyi Duanmu and Xin Feng and Xiaoqing Zhu and Tan, {Wai Tian} and Yao Wang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018 ; Conference date: 10-04-2018 Through 12-04-2018",
year = "2018",
month = jun,
day = "26",
doi = "10.1109/MIPR.2018.00072",
language = "English (US)",
series = "Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "315--320",
booktitle = "Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018",
}