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Method for minimizing entropy in hidden Markov models of physical signals
Method for minimizing entropy in hidden Markov models of physical signals
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机译：最小化物理信号隐马尔可夫模型中的熵的方法
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摘要
A system which observes the world through a video camera and/or other sensors, automatically learns a probabilistic model of normative behavior through the use of a Hidden Markov Model, and uses that model to infer the kind of activity currently under view and detect unusual behavior. The use of hidden Markov models is made possible by entropic training of the model with an &thgr;&thgr; entropic estimator that folds structure learning into the parameter estimation process to remove parameters from the Hidden Markov Model which have little information content, thus to permit real time robust unusual behavior detection. In one embodiment, the system consists of three components: image analysis; model learning; and signal analysis. In image analysis, each frame of video is reduced to a vector of numbers which describe motion of objects in front of the camera, with a sequence of such vectors, one for each frame of video, establishing the “signal.” In model learning, the signal is analyzed to obtain parameters for a probabilistic model of the dynamics of the scene in front of the camera. In signal analysis, the model is used to classify and/or detect anomalies in signals produced on-the-fly by image analysis of new video.
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