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Gender recognition is a hot research topic in recent years. Human-machine interfaces or video surveillance can be greatly improved if human gender can be recognized automatically. In this study, an embedded hidden Markov model is used for gender recognition. Video, which is recorded in different angles of view, is utilized to sample properties of each gender. Ten consecutive gait frames are segmented...
This paper proposes a framework for retrieving semantic video events from indoor surveillance video databases. The goal is to locate video sequences containing events of interest to the user. This framework starts by tracking objects and segmenting videos into Common Appearance Intervals (CAIs). The spatiotemporal trajectories are obtained, based on which features are extracted for the construction...
This paper presents a framework for unsupervised video analysis in the context of dance performances, where gestures and 3D movements of a dancer are characterized by repetition of a set of unknown dance figures. The system is trained in an unsupervised manner using hidden Markov models (HMMs) to automatically segment multiview video recordings of a dancer into recurring elementary temporal body motion...
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