Adaptive background image updating

• US 20040239762A1
• Filed: 05/21/2003
• Published: 12/02/2004
• Est. Priority Date: 05/21/2003
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Porikli, Fatih M., Brinkman, Dirk, Tuzel, Oncel
• Granted Patent: US 7,224,735 B2
• Time in Patent Office: Days
• US Class Current: 348/169
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06V 10/24 Aligning, centring, orientation detection or correction of the image
• CPC Class Codes: G06V 10/28 Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
• CPC Class Codes: G06V 10/62 relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking

Abstract

A method compares a background image to input images to determine a similarity scores λ for each input image. Then, the background image is updated only if the similarity score for a particular image is less than a predetermined threshold. Presumably, any pixel whose color does not change is part of a static background, and any pixel that does change is part of a moving object. The similarity score controls when input images are scored and the manner the background image is updated.

Claims

1. A method for adaptively updating a background image representing a background of a scene, comprising:

determining a similarity score for each of a plurality images of the scene; and

updating the background image only if the similarity score for a particular image is less than a predetermined threshold.

2. The method of claim 1 wherein a rate of when the similarity scores are determined depends on the similarity score.

3. The method of claim 1 wherein a manner in which the background is updated depends on the similarity score.

4. The method of claim 1 further comprising:

initializing the background image to a first one of the plurality of images.

5. The method of claim 1 further comprising:

initializing the background image to a blank image.

6. The method of claim 1 wherein the background image is derived from a reference model in a form of pixel-wise mixture of probability distribution functions.

7. The method of claim 1 further comprising:

identifying pixels in images associated with moving objects;

excluding the identified pixels from the determining and updating.

8. The method of claim 7 wherein the identified pixels are represented by a binary mask.

9. The method of claim 1 further comprising:

selecting a set Q of s pixels in each image;

performing the determining only on the selected set of pixels.

10. The method of claim 9 wherein the pixels in the set are selected randomly.

11. The method of claim 9 wherein the pixels in the set Q are selected uniformly.

12. The method of claim 11 wherein the set Q is

Q→

{B(x_s, y_s,c,t),I(x_s, y_s,c,t)I(x_s,y_s)=(C_1s,m,C_2s,n)M(x_s,y_s,t)≠

1},

where B(x,y,c,t) is the background image I(x,y,c,t) is a particular image M(x,y,t) is an object mask, C_1s, C_2s are sampling frequencies, c is a color channel, t is a frame number associated with the particular image, and s is a number of pixels in the set Q.

13. The method of claim 1 wherein the similarity score is expressed as a ratio

14. The method of claim 1 wherein the similarity score is expressed as a ratio

15. The method of claim 1 wherein the similarity score is expressed as a ratio

16. The method of claim 1 wherein the similarity score is determined by a trained neural network.

17. The method of claim 2 wherein the rate is expressed as a time interval

18. The method of claim 12 further comprising:

determining a blending parameter as

$\begin{array}{cc}\mathrm{\alpha <br><br>}=0.01+\frac{\mathrm{\lambda <br><br>}<br><br>\left(t\right)}{s}& {\mathrm{\tau <br><br>}}_1><br><br>\mathrm{\lambda <br><br>}<br><br>\left(t\right),\end{array}$

where s is the number of pixels in the set Q, λ

is the similarity score at a time t, and τ

₁ is the predetermined threshold.

19. The method of claim 18 wherein a particular pixel p in the background image is updated according to

B(t)=[1−

α

]p(t−

1)+α

I(t).

20. The method of claim 1 wherein similarity scores are determined with a variable and a constant rate.

21. The method of claim 1 wherein only specific regions of the background image are updated.