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Modeling human behavior patterns for detecting the abnormal event has become an important domain in recentyears. A lot of efforts have been made for building smart video surveillance systems with the purpose ofscene analysis and making correct semantic inference from the video moving target. Current approaches havetransferred from rule-based to statistical-based methods with the need of efficient recognition of high-levelactivities. This paper presented not only an update expanding previous related researches, but also a study coveredthe behavior representation and the event modeling. Especially, we provided a new perspective for eventmodeling which divided the methods into the following subcategories: modeling normal event, predictionmodel, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluationschemes used for abnormal behavior detection in intelligent video surveillance. More researches will promotethe development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It isobviously encouraged and dictated by applications of supervising and monitoring in private and public space.The main purpose of this paper is to widely recognize recent available methods and represent the literature ina way of that brings key challenges into notice.

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

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Anomaly detection plays a critical role in intelligent video surveillance. However, real‐world video data obtained always contains large numbers of normal video data, along with large numbers of unlabeled data. A promising solution with one‐class classification and semi‐supervised learning may not be satisfactory as they fail to make good use of unlabeled data with only normal data available. In this paper, we introduced a new framework, called Positive Unlabeled learning‐based Anomaly event Detection (PU‐AD), to exploit the weakly‐supervised information. To the best of our knowledge, this is the first work that introduces the PU idea and achieves detecting abnormal events with a limited number of partially labeled data. Experiments on real‐world surveillance videos show that the proposed method outperforms the existing state‐of‐the‐art methods.