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Action recognition using probabilistic parsing
Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231)
A new approach to the recognition of temporal behaviors and activities is presented. The fundamental idea, inspired by work in speech recognition, is to divide the inference problem into two levels. The lower level is performed using standard independent probabilistic temporal event detectors such as hidden Markov models (HMMs) to propose candidate detections of low level temporal features. The outputs of these detectors provide the input stream for a stochastic contextfree grammar parsing
doi:10.1109/cvpr.1998.698609
dblp:conf/cvpr/BobickI98
fatcat:5ss3274o75ek3jtkrhjbj55tkq