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Action Recognition using Probabilistic Parsing (1998)

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by A. F. Bobick , Y. A. Ivanov
Venue: IEEE CVPR’98
Citations:104 - 6 self
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BibTeX

@MISC{Bobick98actionrecognition,
    author = {A. F. Bobick and Y. A. Ivanov},
    title = { Action Recognition using Probabilistic Parsing},
    year = {1998}
}

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Abstract

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 mechanism. The grammar and parser provide longer range temporal constraints, disambiguate uncertain low level detections, and allow the inclusion of a priori knowledge about the structure of temporal events in a given domain. To achieve such a system we provide techniques for generating a discrete symbol stream from continuous low level detectors, for enforcing temporal exclusion constraints during parsing, and for generating a control method for low level feature application based upon the current parsing state. We demonstrate the approach in several experiments using both visual and other sensing data.

Keyphrases

action recognition    probabilistic parsing    speech recognition    stochastic contextfree grammar    control method    temporal behavior    low level feature application    new approach    range temporal constraint    inference problem    fundamental idea    input stream    priori knowledge    temporal exclusion constraint    uncertain low level detection    discrete symbol stream    hidden markov model    low level temporal feature    several experiment    continuous low level detector    candidate detection    temporal event    current parsing state   

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