Weakly Supervised Anomaly Detection in Videos Considering the Openness of Events
文献类型：期刊论文
| 作者 | Zhang, Chen2,3; Li, Guorong1; Xu, Qianqian4; Zhang, Xinfeng1; Su, Li1; Huang, Qingming1 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2022-05-13 |
| 页码 | 13 |
| 关键词 | Anomaly detection Videos Open data Data models Training Feature extraction Predictive models Anomaly detection surveillance videos openness meta-learning |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2022.3174088 |
| 英文摘要 | Although various weakly supervised anomaly detection methods have been proposed in recent years, generalization of anomaly detection is still not well-explored. Existing weakly supervised methods usually use normal and abnormal events to pose anomaly detection as a regression problem. However, defining concepts that encompass all possible normal and abnormal event patterns is nearly unrealistic, so the anomaly detection model is likely to face both open normal and abnormal events in practical applications. We find some weakly supervised anomaly detection methods suffer from performance degradation when faced with open events due to their poor generalization. To tackle this issue, we propose a two-branch weakly supervised approach, which can improve the anomaly detection performance of open events without affecting the performance of the seen events. Specifically, considering that the pattern of open events is different from that of seen events, we design a Test Data Analyzer (TDA) that determines whether the test video features belong to seen or open data and argue for separate treatment for them. For the seen data, a classifier trained by multiple instance learning is used to predict anomaly scores. For the open data, we design an anomaly detection model via meta-learning named Meta-Learning Anomaly Detection (MLAD), which can directly determine whether open data is abnormal without updating model parameters. In detail, MLAD synthesizes pseudo-seen data and pseudo-open data so that the model can learn to detect anomalies in open data by transferring the knowledge of seen data. Experimental results validate the effectiveness of our proposed method. |
| 资助项目 | Italy-China Collaboration Project Talent[2018YFE0118400] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61976069] ; National Natural Science Foundation of China[61872333] ; National Natural Science Foundation of China[61931008] ; Youth Innovation Promotion Association CAS ; Fundamental Research Funds for Central Universities |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000799606400001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19568]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Guorong |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 2.Chinese Acad Sci, State Key Lab Informat Security, Inst Informat Engn, Beijing 100093, Peoples R China 3.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Chen,Li, Guorong,Xu, Qianqian,et al. Weakly Supervised Anomaly Detection in Videos Considering the Openness of Events[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:13. |
| APA | Zhang, Chen,Li, Guorong,Xu, Qianqian,Zhang, Xinfeng,Su, Li,&Huang, Qingming.(2022).Weakly Supervised Anomaly Detection in Videos Considering the Openness of Events.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13. |
| MLA | Zhang, Chen,et al."Weakly Supervised Anomaly Detection in Videos Considering the Openness of Events".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):13. |
入库方式： OAI收割
来源：计算技术研究所
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