Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies
Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies
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DOI：
10.24963/ijcai.2019/419
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发表时间：
2019-08
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影响因子：
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通讯作者：
Wen Liu;Weixin Luo;Zhengxin Li;P. Zhao;Shenghua Gao
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作者：
Wen Liu;Weixin Luo;Zhengxin Li;P. Zhao;Shenghua Gao
经典的半监督视频异常检测假设训练集中只有正常数据可用，因为异常的罕见性和无界性。然而，很明显，这些不经常观察到的异常事件实际上可以帮助检测相同或类似的异常事件，这一思路促使我们研究开放集监督异常检测，只有少数类型的异常观察事件和许多正常事件可用。在假设正常事件可以很好地预测的前提下，我们提出了一种边际学习嵌入预测（MLEP）框架。基于MLEP的开放集监督视频异常检测有三个特点：1)我们定制了一个有利于正常事件预测和扭曲异常事件预测的视频预测框架；ii)边际学习框架学习更紧凑的正态数据分布，并扩大正常事件与异常事件之间的边际。由于异常事件是无界的，因此我们的框架有助于检测异常事件，甚至是以前从未观察到的异常。因此，我们的框架适用于开集监督异常检测设置；Iii)我们的框架可以很容易地处理帧级和视频级的异常注释。考虑到视频级异常检测在实践中更容易标注，而少数异常的异常检测是一种更实际的设置，因此我们的工作将异常检测的应用推向了真实场景。大量的实验验证了我们的异常检测框架的有效性。
Classical semi-supervised video anomaly detection assumes that only normal data are available in the training set because of the rare and unbounded nature of anomalies. It is obviously, however, these infrequently observed abnormal events can actually help with the detection of identical or similar abnormal events, a line of thinking that motivates us to study open-set supervised anomaly detection with only a few types of abnormal observed events and many normal events available. Under the assumption that normal events can be well predicted, we propose a Margin Learning Embedded Prediction (MLEP) framework. There are three features in MLEP- based open-set supervised video anomaly detection: i) we customize a video prediction framework that favors the prediction of normal events and distorts the prediction of abnormal events; ii) The margin learning framework learns a more compact normal data distribution and enlarges the margin between normal and abnormal events. Since abnormal events are unbounded, our framework consequently helps with the detection of abnormal events, even for anomalies that have never been previously observed. Therefore, our framework is suitable for the open-set supervised anomaly detection setting; iii) our framework can readily handle both frame-level and video-level anomaly annotations. Considering that video-level anomaly detection is more easily annotated in practice and that anomaly detection with a few anomalies is a more practical setting, our work thus pushes the application of anomaly detection towards real scenarios. Extensive experiments validate the effectiveness of our framework for anomaly detection.