@inproceedings{34ab4f065535462ca5dd4e20d935a0b1,
title = "A Novel Ant Colony Detection Using Multi-Region Histogram for Object Tracking",
abstract = "Efficient object tracking become more popular in video processing domain. In recent years, many researchers have developed excellent models and methods for complicated tracking problems in real environment. Among those approaches, object feature definition is one of the most important component to obtain better accuracy in tracking. In this paper, we propose a novel multi-region feature selection method which defines histogram values of basic areas and random areas (MRH) and combined with continuous ant colony filter detection to represent the original target. The proposed approach also achieves smooth tracking on different video sequences, especially with Motion Blur problem. This approach is designed and tested in MATLAB 2016b environment. The experiment result demonstrates better and faster tracking performance and shows continuous tracking trajectory and competitive outcomes regarding to traditional methods.",
keywords = "Ant colony filter, Histogram, Multi-Region Histogram",
author = "Zandavi, {Seid Miad} and Feng Sha and Vera Chung and Zhicheng Lu and Weiming Zhi",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70090-8_3",
language = "English",
isbn = "9783319700892",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag Italia Srl",
pages = "25--33",
editor = "Derong Liu and Shengli Xie and El-Alfy, {El-Sayed M.} and Dongbin Zhao and Yuanqing Li",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
address = "Italy",
}