METHOD OF OCCLUSION-BASED BACKGROUND MOTION ESTIMATION

• US 20140126818A1
• Filed: 11/06/2012
• Published: 05/08/2014
• Est. Priority Date: 11/06/2012
• Status: Abandoned Application

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Wei, Jianing
• Application Number:
  US13/670,296
• Publication Number:
  US 20140126818A1
• Time in Patent Office: Days
• US Class Current: 382/171
• CPC Class Codes: G06T 7/215 Motion-based segmentation

Abstract

A technique for estimating background motion in monocular video sequences is described herein. The technique is based on occlusion information contained in video sequences. Two algorithms are described for estimating background motion: one fits well for general cases, and the other fits well for a case when available memory is very limited. The significance of the technique includes: a motion segmentation algorithm with adaptive and temporally stable estimate of the number of objects is developed, two algorithms are developed to infer occlusion relations among segmented objects using the detected occlusions and background motion estimation from the inferred occlusion relations.

Claims

1. A method of motion estimation programmed in a memory of a device comprising:

a. performing motion segmentation to segment an image into different objects using motion vectors to obtain a segmentation result;

b. generating an occlusion matrix using the segmentation result, occluded pixel information and image data; and

c. estimating background motion using the occlusion matrix.

2. The method of claim 1 wherein the occlusion matrix is of size K×

K, wherein K is a number of objects in the image.

3. The method of claim 1 wherein each entry in the occlusion matrix represents the number of pixels one segment occludes another segment.

4. The method of claim 1 wherein estimating the motion of the background object includes finding the background object.

5. The method of claim 1 wherein the device is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, a smart phone, a portable music player, a tablet computer, a mobile device, a video player, a video disc writer/player, a television, and a home entertainment system.

6. A method of motion segmentation programmed in a memory of a device comprising:

a. generating a histogram using input motion vectors;

b. performing K-means clustering with a different number of clusters and generating a cost;

c. determining a number of clusters using the cost;

d. computing a centroid of each cluster; and

e. clustering a motion vector at each pixel with a nearest centroid, wherein the clustered motion vector and nearest centroid segments a frame into object.

7. The method of claim 6 wherein a number of the segments is not fixed.

8. The method of claim 6 wherein a temporally stable estimation of the number of clusters is developed.

9. The method of claim 6 wherein a Bayesian approach for estimation is used.

10. The method of claim 6 wherein the device is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, a smart phone, a portable music player, a tablet computer, a mobile device, a video player, a video disc writer/player, a television, and a home entertainment system.

11. A method of occlusion relation inference programmed in a memory of a device comprising:

a. finding a first corresponding motion segment of an occluding object;

b. finding a pixel location in the next frame;

c. finding a second corresponding motion segment of the occluded object;

d. incrementing an entry in an occlusion matrix; and

e. repeating the steps a-d until all occlusion pixels have been traversed.

12. The method of claim 11 wherein the entry represents the number of pixels a first segment occludes a second segment.

13. The method of claim 11 wherein the device is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, a smart phone, a portable music player, a tablet computer, a mobile device, a video player, a video disc writer/player, a television, and a home entertainment system.

14. A method of occlusion relation inference programmed in a memory of a device comprising:

a. using a sliding window to locate occlusion regions and neighboring regions;

b. moving the window if there are no occluded pixels are in the window;

c. computing a first luminance histogram at the occluded pixels;

d. computing a second luminance histogram for each motion segment inside the window;

e. comparing the first luminance histogram and the second luminance histogram;

f. identifying a first motion segment with a closest luminance histogram to an occlusion region as a background object in the window;

g. identifying a second motion segment with the most pixels among all but background motion segments as an occluding, foreground object;

h. incrementing an entry in an occlusion matrix by the number of pixels in the occlusion region in the window; and

i. repeating the steps a-h until an entire frame has been traversed.

15. The method of claim 14 wherein the device is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, a smart phone, a portable music player, a tablet computer, a mobile device, a video player, a video disc writer/player, a television, and a home entertainment system.

16. A method of background motion estimation programmed in a memory of a device comprising:

a. designing a metric to measure an amount of contradiction when selecting a motion segment as a background object;

b. assigning a background motion to be the motion segment with a minimum amount of contradiction; and

c. subtracting the background motion of the background object from motion vectors to obtain a depth map.

17. The method of claim 16 further comprising determining if the number of occluded pixels is below a first threshold or a minimum contradiction is above a second threshold, or determining if a total number of occlusion pixels is below a third threshold, then assigning the background object to be a largest segment, and a corresponding motion is assigned to be the background motion.

18. The method of claim 16 wherein the device is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, a smart phone, a portable music player, a tablet computer, a mobile device, a video player, a video disc writer/player, a television, and a home entertainment system.

19. An apparatus comprising:

a. a video acquisition component for acquiring a video;

b. a memory for storing an application, the application for;

i. performing motion segmentation to segment an image of the video into different objects using motion vectors to obtain a segmentation result;

ii. generating an occlusion matrix using the segmentation result, occluded pixel information and image data; and

iii. estimating the background motion using the occlusion matrix; and

c. a processing component coupled to the memory, the processing component configured for processing the application.

20. The apparatus of claim 19 wherein the occlusion matrix is of size K×

K, wherein K is a number of objects in the image.

21. The apparatus of claim 19 wherein each entry in the occlusion matrix represents the number of pixels one segment occludes another segment.

22. The apparatus of claim 19 wherein estimating the background motion includes finding the background object.