Dynamic generative process modeling, tracking and analyzing
First Claim
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1. A method for modeling a generative process dynamically, comprising:
- acquiring time series data generated by a generative process;
sampling the time series data to extract a single feature vector for each instance in time while acquiring, the feature vector including a plurality of dependent features of the time series data, the sampling using a sliding window for each instance in time; and
updating dynamically a multivariate model according to the single feature vector for each instance in time while acquiring and sampling, the multivariate model including a mixture of Gaussian distribution functions.
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Abstract
A method tracks and analyzes dynamically a generative process that generates multivariate time series data. In one application, the method is used to detect boundaries in broadcast programs, for example, a sports broadcast and a news broadcast. In another application, significant events are detected in a signal obtained by a surveillance device, such as a video camera or microphone.
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Citations
16 Claims
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1. A method for modeling a generative process dynamically, comprising:
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acquiring time series data generated by a generative process;
sampling the time series data to extract a single feature vector for each instance in time while acquiring, the feature vector including a plurality of dependent features of the time series data, the sampling using a sliding window for each instance in time; and
updating dynamically a multivariate model according to the single feature vector for each instance in time while acquiring and sampling, the multivariate model including a mixture of Gaussian distribution functions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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Specification