Dynamic generative process modeling, tracking and analyzing

• US 20070010998A1
• Filed: 07/08/2005
• Published: 01/11/2007
• Est. Priority Date: 07/08/2005
• Status: Abandoned Application

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Divakaran, Ajay, Radhakrishnan, Regunathan
• Application Number:
  US11/177,917
• Publication Number:
  US 20070010998A1
• Time in Patent Office: Days
• US Class Current: 704/211
• CPC Class Codes: G06V 20/40 in video content extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: H04N 5/147 Scene change detection

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.

Claims

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.

2. The method of claim 1, in which the time series data is a broadcast signal including a plurality of programs, and further comprising:

detecting dynamically boundaries between the plurality of programs using the multivariate model, while acquiring, sampling and updating.

3. The method of claim 2, further comprising:

recording dynamically only selected programs between the program boundaries while acquiring, sampling and updating.

4. The method of claim 1, in which the time series data is a real-time surveillance signal, and further comprising:

detecting dynamically significant events in the real-time surveillance signal using the multivariate model while acquiring, sampling and updating.

5. The method of claim 4, further comprising:

generating an alarm signal in response to detecting the significant events.

6. The method of claim 1, in which the time series data is a broadcast signal including a program and a plurality of commercials;

detecting dynamically boundaries between the program and the plurality of commercials using the multivariate model while acquiring, sampling and updating; and

recording only the program.

7. The method of claim 1, in which the time series data is a broadcast signal including audio and video signals.

8. The method of claim 1, in which the time series data are acquired by a plurality of sensors.

9. The method of claim 1, further comprising:

adjusting dynamically a size of the sliding window and a rate of sampling of the time series data according to the multivariate model while acquiring, sampling and updating.

10. The method of claim 1, further comprising:

adjusting dynamically the types of the plurality of dependent features according to the multivariate model while acquiring, sampling and updating.

11. The method of claim 1, further comprising:

analyzing dynamically the multivariate model to generate a control signal while acquiring, sampling and updating.

12. The method of claim 11, further comprising:

processing dynamically the time series data according to the control signal while acquiring, sampling and updating.

13. The method of claim 1, in which a number of Gaussian distribution functions is determined according to a minimum description length principle.

14. The method of claim 1, in which each one of K Gaussian probability functions is denoted by a set of parameters, the sets of parameters including probability coefficients {π

_k}_k=1^K, means {μ

_k}_k=1^K, and variances {R_k}_k=1^K.

15. The method of claim 1, further comprising:

determining a likelihood for each feature vector using the multivariate model; and

updating the multivariate model according to the likelihood.

16. The method of claim 1, in which each feature vector includes low level features and high level features, and further comprising:

detecting a candidate change in the multivariate model using the high level features; and

verifying the candidate change using the low level features.