Quantifying and detecting collective motion by manifold learning | |
Wang, Qi1; Chen, Mulin1; Li, Xuelong2; Wang, Qi (crabwq@gmail.com) | |
2017 | |
会议名称 | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
会议录名称 | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
页码 | 4292-4298 |
会议日期 | 2017-02-04 |
会议地点 | San Francisco, CA, United states |
出版者 | AAAI press |
产权排序 | 2 |
摘要 | The analysis of collective motion has attracted many researchers in artificial intelligence. Though plenty of works have been done on this topic, the achieved performance is still unsatisfying due to the complex nature of collective motions. By investigating the similarity of individuals, this paper proposes a novel framework for both quantifying and detecting collective motions. Our main contributions are threefold: (1) the time-varying dynamics of individuals are deeply investigated to better characterize the individual motion; (2) a structure-based collectiveness measurement is designed to precisely quantify both individual-level and scene-level properties of collective motions; (3) a multi-stage clustering strategy is presented to discover a more comprehensive understanding of the crowd scenes, containing both local and global collective motions. Extensive experimental results on real world data sets show that our method is capable of handling crowd scenes with complicated structures and various dynamics, and demonstrate its superior performance against state-of-the-art competitors. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
作者部门 | 光学影像学习与分析中心 |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29399 |
专题 | 光谱成像技术研究室 |
通讯作者 | Wang, Qi (crabwq@gmail.com) |
作者单位 | 1.School of Computer Science, Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China 2.Center for OPTical IMagery Analysis and Learning (OPTIMAL), Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China |
推荐引用方式 GB/T 7714 | Wang, Qi,Chen, Mulin,Li, Xuelong,et al. Quantifying and detecting collective motion by manifold learning[C]:AAAI press,2017:4292-4298. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Quantifying and dete(4769KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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