Abnormal human behavior detection in videos: A review

Abstract

Modeling human behavior patterns for detecting the abnormal event has become an important domain in recent years. A lot of efforts have been made for building smart video surveillance systems with the purpose of scene analysis and making correct semantic inference from the video moving target. Current approaches have transferred from rule-based to statistical-based methods with the need of efficient recognition of high-level activities. This paper presented not only an update expanding previous related researches, but also a study covered the behavior representation and the event modeling. Especially, we provided a new perspective for event modeling which divided the methods into the following subcategories: Modeling normal event, prediction model, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluation schemes used for abnormal behavior detection in intelligent video surveillance. More researches will promote the development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It is obviously 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 in a way of that brings key challenges into notice.