Learning to Detect Anomalies in Surveillance Video
Learning to Detect Anomalies in Surveillance Video
复制标题
学习检测监控视频中的异常情况
DOI：
10.1109/lsp.2015.2410031
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发表时间：
2015-03
影响因子：
3.9
通讯作者：
Zha, Hongbin
中科院分区：
文献类型：
--
作者：
Xiao, Tan;Zhang, Chao;Zha, Hongbin
Detecting anomalies in surveillance videos, that is, finding events or objects with low probability of occurrence, is a practical and challenging research topic in computer vision community. In this paper, we put forward a novel unsupervised learning framework for anomaly detection. At feature level, we propose a Sparse Semi-nonnegative Matrix Factorization (SSMF) to learn local patterns at each pixel, and a Histogram of Nonnegative Coefficients (HNC) can be constructed as local feature which is more expressive than previously used features like Histogram of Oriented Gradients (HOG). At model level, we learn a probability model which takes the spatial and temporal contextual information into consideration. Our framework is totally unsupervised requiring no human-labeled training data. With more expressive features and more complicated model, our framework can accurately detect and localize anomalies in surveillance video. We carried out extensive experiments on several benchmark video datasets for anomaly detection, and the results demonstrate the superiority of our framework to state-of-the-art approaches, validating the effectiveness of our framework.
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DOI：
--
发表时间：
2012-12
期刊：
--
影响因子：
--
作者：
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通讯作者：
Xiaofeng Ren;Liefeng Bo
DOI：
10.1109/cvpr.2009.5206569
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
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期刊：
--
影响因子：
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DOI：
--
发表时间：
2000
期刊：
--
影响因子：
--
作者：
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通讯作者：
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影响因子：
8
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