@inproceedings{e03e51a0655042fea98914feffdfea8f,
title = "Multiple object tracking based on robust network flow model",
abstract = "Network flow model (NF) with the pairwise property has been paid more attention by researchers in recent years. However, the pairwise will bring a detrimental tracking performance under the occlusion case, such as the phenomenon of ID switches (IDs) and frequent appearing of fragments. Referring to the pairwise, we propose the robust network flow (RNF) model, which is based on a prior that every candidate detection has a correlative degree with the current tracking path. Based on the correlative degree, the cost of the pairwise nodes is recomputed and the best candidate detection can be found. The experimental results on four challenging video sequences show that our method is able to obtain the superior performance under the occlusion case.",
keywords = "Robust network flow, Visual object tracking",
author = "Shuangyan Yi and Hongpeng Wang and Zhenyu He and Yi Li and Chen, \{Wen Sheng\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 12th International Conference on Computational Intelligence and Security, CIS 2016 ; Conference date: 16-12-2016 Through 19-12-2016",
year = "2017",
month = jan,
day = "17",
doi = "10.1109/CIS.2016.73",
language = "英语",
series = "Proceedings - 12th International Conference on Computational Intelligence and Security, CIS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "299--303",
booktitle = "Proceedings - 12th International Conference on Computational Intelligence and Security, CIS 2016",
address = "美国",
}