METHOD AND APPARATUS FOR OBJECT DETECTION USING COMPRESSIVE SENSING

• US 20130156261A1
• Filed: 12/16/2011
• Published: 06/20/2013
• Est. Priority Date: 12/16/2011
• Status: Abandoned Application

• Current Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Original Assignee: Alcatel-Lucent USA, Inc. (Nokia Corporation)
• Inventors: Jiang, Hong, Wilford, Paul
• Application Number:
  US13/328,149
• Publication Number:
  US 20130156261A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06F 18/2415 based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate

Abstract

In one embodiment, the method for object detection and compressive sensing includes receiving, by a decoder, measurements. The measurements are coded data that represents video data. The method further includes estimating, by the decoder, probability density functions based upon the measurements. The method further includes identifying, by the decoder, a background image and at least one foreground image based upon the estimated probability density functions. The method further includes examining the at least one foreground image to detect at least one object of interest.

Claims

1. A method of detecting at least one object of interest within data in a communication network, comprising:

receiving, by a decoder, a set of measurements, the set of measurements being coded data representing video data;

estimating, by the decoder, probability density functions based upon the set of measurements;

identifying, by the decoder, a background image and at least one foreground image based upon the estimated probability density functions; and

examining the at least one foreground image to detect at least one object of interest.

2. The method of claim 1, wherein the estimating comprises:

obtaining, by the decoder, a range of pixel values of video data that satisfy an expression characterizing a relationship based upon the set of measurements;

determining intermediate functions based upon the range of pixel values; and

performing a convolution of the intermediate functions to obtain the estimated probability density functions.

3. The method of claim 1, wherein the estimating comprises:

obtaining, by the decoder, estimated pixel values of the video data that satisfy a minimization problem; and

determining, by the decoder, histograms based upon the estimated pixel values, the histograms representing the estimated probability distribution functions.

4. The method of claim 1, wherein the estimating models the estimated probability density functions as a mixture Gaussian distribution.

5. The method of claim 1, wherein the identifying identifies the background image using a mathematical mode of the estimated probability density functions.

6. The method of claim 1, wherein the examining comprises:

obtaining, by the decoder, estimated pixel values of the video data that satisfy a minimization problem;

obtaining, by the decoder, at least one foreground image by subtracting the background image from the estimated pixel values of the video data; and

examining the at least one foreground image to detect at least one object of interest.

7. The method of claim 1, wherein the examining comprises:

obtaining, by the decoder, a range of pixel values of video data that satisfy an expression characterizing a relationship based upon the set of measurements;

determining, by the decoder, a shape property and a motion property of the at least one foreground object; and

examining the shape property and the motion property of the at least one foreground object to detect at least one object of interest.

8. The method of claim 1, wherein the video data is luminance data.

9. The method of claim 1, wherein the video data is chrominance data.

10. An apparatus for detecting at least one object of interest within video data, the apparatus comprising:

a decoder configured to receive a set of measurements, the measurements being coded data representing the video data,

the decoder configured to estimate probability density functions for the video data based upon the set of measurements,

the decoder configured to identify a background image and at least one foreground image based upon the estimated probability density functions, and

the decoder configured to examine the at least one foreground image to detect at least one object of interest.

11. The apparatus of claim 10, wherein the decoder is further configured to:

obtain a range of pixel values of video data that satisfy an expression characterizing a relationship based upon the set of measurements;

determine intermediate functions based upon the range of pixel values; and

perform a convolution of the intermediate functions to obtain the estimated probability density functions.

12. The apparatus of claim 10, wherein the decoder is further configured to:

obtain estimated pixel values of the video data that satisfy a minimization problem;

determine histograms based upon the estimated pixel values, the histograms representing the estimated probability distribution functions.

13. The apparatus of claim 10, wherein the decoder is configured to model the estimated probability density functions as a mixture Gaussian distribution.

14. The apparatus of claim 10, wherein the decoder is configured to identify the background image using a mathematical mode of the estimated probability density functions.

15. The apparatus of claim 10, wherein the decoder is further configured to:

obtain estimated pixel values of the video data that satisfy a minimization problem;

obtain at least one foreground image by subtracting the background image from the estimated pixel values of the video data; and

examine the at least one foreground image to detect at least one object of interest.

16. The apparatus of claim 10, wherein the decoder is further configured to:

obtain a range of pixel values of video data that satisfy an expression characterizing a relationship based upon the set of measurements;

determine a shape property and a motion property of the at least one foreground object; and

examine the shape property and the motion property of the at least one foreground object to detect at least one object of interest.

17. The apparatus of claim 10, wherein the video data is luminance data.

18. The apparatus of claim 10, wherein the video data is chrominance data.