Statistical Hypothesis Detector for Abnormal Event Detection in Crowded Scenes | |
Yuan, Yuan1; Feng, Yachuang1,2; Lu, Xiaoqiang1 | |
作者部门 | 光学影像学习与分析中心 |
2017-11-01 | |
发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS |
ISSN | 2168-2267 |
卷号 | 47期号:11页码:3597-3608 |
产权排序 | 1 |
摘要 | Abnormal event detection is now a challenging task, especially for crowded scenes. Many existing methods learn a normal event model in the training phase, and events which cannot be well represented are treated as abnormalities. However, they fail to make use of abnormal event patterns, which are elements to comprise abnormal events. Moreover, normal patterns in testing videos may be divergent from training ones, due to the existence of abnormalities. To address these problems, in this paper, an abnormality detector is proposed to detect abnormal events based on a statistical hypothesis test. The proposed detector treats each sample as a combination of a set of event patterns. Due to the unavailability of labeled abnormalities for training, abnormal patterns are adaptively extracted from incoming unlabeled testing samples. Contributions of this paper are listed as follows: 1) we introduce the idea of a statistical hypothesis test into the framework of abnormality detection, and abnormal events are identified as ones containing abnormal event patterns while possessing high abnormality detector scores; 2) due to the complexity of video events, noise seldom follows a simple distribution. For this reason, we approximate the complex noise distribution by employing a mixture of Gaussian. This benefits the modeling of video events and improves abnormality detection accuracies; and 3) because of the existence of abnormalities, there are always some unusually occurring normal events in the testing videos, which differ from the training ones. To represent normal events precisely, an online updating strategy is proposed to cover these cases in the normal event patterns. As a result, false detections are eliminated mostly. Extensive experiments and comparisons with state-of-the-art methods verify the effectiveness of the proposed algorithm. |
文章类型 | Article |
关键词 | Abnormal Event Detection Abnormality Detector Mixture Of Gaussian (Mog) Statistical Hypothesis Test |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TCYB.2016.2572609 |
收录类别 | SCI ; EI |
关键词[WOS] | ANOMALY DETECTION ; SPARSE REPRESENTATION ; BEHAVIOR DETECTION ; OBJECT TRACKING ; MODEL ; ALGORITHMS ; PATTERNS |
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 Basic Research Program of China (973 Program)(2012CB719905) ; 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) |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
WOS记录号 | WOS:000413003100010 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29369 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, 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 | Yuan, Yuan,Feng, Yachuang,Lu, Xiaoqiang. Statistical Hypothesis Detector for Abnormal Event Detection in Crowded Scenes[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(11):3597-3608. |
APA | Yuan, Yuan,Feng, Yachuang,&Lu, Xiaoqiang.(2017).Statistical Hypothesis Detector for Abnormal Event Detection in Crowded Scenes.IEEE TRANSACTIONS ON CYBERNETICS,47(11),3597-3608. |
MLA | Yuan, Yuan,et al."Statistical Hypothesis Detector for Abnormal Event Detection in Crowded Scenes".IEEE TRANSACTIONS ON CYBERNETICS 47.11(2017):3597-3608. |
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 | ||
Statistical Hypothes(1891KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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