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Encoding Human Motion for Automated Activity Recognition in Surveillance Applications
Abstract
As computing becomes ubiquitous in our modern society, automated recognition of human activities emerges as a crucial topic where it can be applied to many real-life human-centric scenarios such as smart automated surveillance, human computer interaction and automated refereeing. Although the perception of activities is spontaneous for the human visual system, it has proven to be extraordinarily difficult to duplicate this capability into computer vision systems for automated understanding of human behavior. Motion pictures provide even richer and reliable information for the perception of the different biological, social and psychological characteristics of the person such as emotions, actions and personality traits of the subject. In spite of the fact that there is a considerable body of work devoted to human action recognition, most of the methods are evaluated on datasets recorded in simplified settings. More recent research has shifted focus to natural activity recognition in unconstrained scenes with more complex settings.
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