Learning deep event models for crowd anomaly detection | |
Feng, Yachuang1,2; Yuan, Yuan1; Lu, Xiaoqiang1; Lu, Xiaoqiang (luxq666666@gmail.com) | |
作者部门 | 光学影像学习与分析中心 |
2017-01-05 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
卷号 | 219页码:548-556 |
产权排序 | 1 |
摘要 | Abnormal event detection in video surveillance is extremely important, especially for crowded scenes. In recent years, many algorithms have been proposed based on hand-crafted features. However, it still remains challenging to decide which kind of feature is suitable for a specific situation. In addition, it is hard and time-consuming to design an effective descriptor. In this paper, video events are automatically represented and modeled in unsupervised fashions. Specifically, appearance and motion features are simultaneously extracted using a PCANet from 3D gradients. In order to model event patterns, a deep Gaussian mixture model (GMM) is constructed with observed normal events. The deep GMM is a scalable deep generative model which stacks multiple GMM-layers on top of each other. As a result, the proposed method acquires competitive performance with relatively few parameters. In the testing phase, the likelihood is calculated to judge whether a video event is abnormal or not. In this paper, the proposed method is verified on two publicly available datasets and compared with state-of-the-art algorithms. Experimental results show that the deep model is effective for abnormal event detection in video surveillance. |
文章类型 | Article |
关键词 | Deep Neural Network Pcanet Deep Gmm Crowded Scene Abnormal Event Detection Video Surveillance |
学科领域 | Digital Computers And Systems |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1016/j.neucom.2016.09.063 |
收录类别 | SCI ; EI |
关键词[WOS] | SCENES ; LOCALIZATION |
语种 | 英语 |
WOS研究方向 | Computer Science |
项目资助者 | National Basic Research Program of China (Youth 973 Program)(2013CB336500) ; State Key Program of National Natural Science of China(61232010) ; National Natural Science Foundation of China(61472413) ; Key Research Program of the Chinese Academy of Sciences(KGZD-EW-T03) ; Open Research Fund of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences(LSIT201408) ; Young Top-notch Talent Program of Chinese Academy of Sciences(QYZDB-SSW-JSC015) |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000390734300050 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28512 |
专题 | 光谱成像技术研究室 |
通讯作者 | Lu, Xiaoqiang (luxq666666@gmail.com) |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Feng, Yachuang,Yuan, Yuan,Lu, Xiaoqiang,et al. Learning deep event models for crowd anomaly detection[J]. NEUROCOMPUTING,2017,219:548-556. |
APA | Feng, Yachuang,Yuan, Yuan,Lu, Xiaoqiang,&Lu, Xiaoqiang .(2017).Learning deep event models for crowd anomaly detection.NEUROCOMPUTING,219,548-556. |
MLA | Feng, Yachuang,et al."Learning deep event models for crowd anomaly detection".NEUROCOMPUTING 219(2017):548-556. |
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 | ||
Learning deep event (946KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论