Titlebook: Anomaly Detection in Video Surveillance; Xiaochun Wang Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive license

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China Takes Charge of a Changing Epidemic,kground remains fixed for a certain period of time, an optical flow method is introduced in this chapter for anomaly detection to achieve good recognition and localization performances for more dynamic and cluttered scenes. Not only can the optical flow algorithms be used to directly detect abnormal

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Hong Hu,Guy Taylor,Qingfeng Chenrobabilities. In this chapter, we see another method of probability estimation-based abnormal behavior detection called Markov random field. Basically, if the human behaviors can be segmented into a sequence of discrete states, this Markov model can be used as one of the most popular ways of represe

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Fujie Zhang,Ye Ma,Yan Zhao,Willa Dongcenes. Generally, crowd abnormal behaviors cannot be treated as a simple collection of individual behaviors in the form of trajectories due to the occlusion that happens among them. One solution would be to treat the crowd as a single entirety in a specific scene and detect the anomaly by analyzing

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Fujie Zhang,Ye Ma,Yan Zhao,Willa Dong a single-class nonparametric method that finds the boundary around a dataset that encloses the normal samples so as to establish the separation between two classes, that is, the normal events and the abnormal events in the video anomaly detection applications, that is, models the support of the dis

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Fujie Zhang,Ye Ma,Yan Zhao,Willa Dongvior recognition. Individual abnormal behavior recognition focuses on the classification of a single abnormal behavior or behavior trajectory that is the result produced by a single individual or a few persons. In crowd abnormal behavior recognition, much more individuals are involved and the abnorm

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https://doi.org/10.1057/9781137504210ation-based anomaly detection that continues the work presented in Chap. . by using information extracted from observed normal data through deep neural networks to provide more effective and efficient anomaly detection solutions. We will look at a number of deep neural network architectures principa