Methods and systems for identifying background in video data using geometric primitives

1. A method comprising:

obtaining video data comprising at least one frame of pixel data and depicting at least a portion of a user;

processing the video data with at least one persona identification module to generate at least one persona probability map, the at least one persona identification module comprising a geometric primitive module for generating a first persona probability map at least in part by:

detecting at least one geometric primitive depicted within the video data, wherein the at least one detected geometric primitive is a type of geometric primitive included in a set of geometric-primitive models;

identifying a respective region within the video data associated with each of the at least one detected geometric primitives; and

assigning the respective regions an increased background-probability in the first persona probability map; and

outputting a persona image depicting at least the portion of the user by extracting pixels from the at least one frame of pixel data based on the at least one persona probability map.

2. The method of claim 1 , wherein obtaining the video data comprises obtaining the video data via at least one of a video camera, a depth camera, a 3-D camera, and a data store.

3. The method of claim 1 , wherein the set of geometric-primitive models includes a straight line.

4. The method of claim 3 , wherein:

at least one of the detected geometric primitives is a straight line; and

the respective region within the video data associated with the detected straight line is a rectangle.

5. The method of claim 4 , wherein the rectangle (i) has a length that is equal to a length of the detected straight line and (ii) is bisected by the detected straight line.

6. The method of claim 4 , wherein the rectangle (i) has a length that is less than a length of the detected straight line and (ii) is bisected by the detected straight line.

7. The method of claim 1 , wherein the set of geometric-primitive models includes a straight line longer than a threshold length.

8. The method of claim 1 , wherein the set of geometric-primitive models includes an angle.

9. The method of claim 8 , wherein:

at least one of the detected geometric primitives is an angle; and

the respective region within the video data associated with the detected angle is a triangle that is made up of two line segments that form the detected angle and one line segment that connects the two line segments that form the detected angle.

10. The method of claim 8 , wherein:

at least one of the detected geometric primitives is an angle; and

the respective region within the video data associated with the detected angle is a sub-region of a triangle that is made up of two line segments that form the detected angle and one line segment that connects the two line segments that form the detected angle.

11. The method of claim 10 , wherein the sub-region is a quadrilateral sub-region formed by two triangular sub-regions sharing one common side.

12. The method of claim 1 , wherein the set of geometric-primitive models include an angle within a threshold tolerance of being a right angle.

13. The method of claim 1 , wherein the set of geometric-primitive models includes an angle made up of two line segments, wherein each of the two line segments is longer than a threshold length.

14. The method of claim 1 , further comprising updating a background-color model at least in part by using at least one identified respective region.

15. The method of claim 14 , wherein updating the background-color model comprises:

identifying a respective color of at least one pixel included in the at least one identified respective region; and

adding to the background-color model the identified respective color of the at least one pixel.

16. The method of claim 1 , wherein detecting at least one geometric primitive comprises edge detection using high-frequency-information recognition.

17. The method of claim 1 , wherein the set of geometric-primitive models comprises at least one of: right angles, long straight lines, squares, triangles, and circles.

18. The method of claim 1 , wherein the set of geometric-primitive models define shapes or geometric structures that are not part of an extracted persona.

19. A system comprising:

a communication interface;

a processor; and

computer-readable non-transitory data storage containing instructions executable by the processor for causing the system to carry out a set of functions, the set of functions including:

obtaining video data comprising at least one frame of pixel data and depicting at least a portion of a user;

processing the video data with at least one persona identification module to generate at least one persona probability map, the at least one persona identification module comprising a geometric primitive module for generating a first persona probability map at least in part by:

detecting at least one geometric primitive depicted within the video data, wherein the at least one detected geometric primitive is a type of geometric primitive included in a set of geometric-primitive models;

identifying a respective region within the video data associated with each of the at least one detected geometric primitives; and

assigning the respective regions an increased background-probability in the first persona probability map; and

outputting a persona image depicting at least the portion of the user by extracting pixels from the at least one frame of pixel data based on the at least one persona probability map.