Visual target tracking using model fitting and exemplar

1. A method of tracking a human target, the method comprising:

receiving an observed depth image of a scene from a source;

body scanning the scene to identify a human target in the scene;

removing non-target background information from the observed depth image;

applying to the observed depth image one or more decision trees trained from a collection of known poses to find an exemplar pose that represents an observed pose of the human target;

rasterizing a body model of the human target into a synthesized depth image having a rasterized pose;

adjusting the rasterized pose of the body model into a model-fitting pose based, at least in part, on differences between the observed depth image and the synthesized depth image;

comparing a confidence in the exemplar pose to a confidence in the model-fitting pose;

selecting the exemplar pose if the confidence in the exemplar pose is higher than or equal to the confidence in the model-fitting pose; and

selecting the model-fitting pose if the confidence in the model-fitting pose is higher than the confidence in the exemplar pose.

2. The method of claim 1 , where a terminal node of a decision tree yields a best-guess of a body part for a pixel and a confidence that the best-guess is correct.

3. The method of claim 1 , where removing non-target background information from the observed depth image includes removing depth image information outside of a sphere surrounding the target.

4. The method of claim 1 , further comprising analyzing the observed depth image with a hand-identifying algorithm configured to identify hands on the human target; and

increasing relative confidence of the exemplar pose if the exemplar pose more closely places hands in a same location as the hand-identifying algorithm; and

increasing relative confidence of the model-fitting pose if the model-fitting pose more closely places hands in a same location as the hand-identifying algorithm.

5. A method of tracking a target, the method comprising:

receiving an observed depth image of a target from a source;

analyzing the observed depth image with a prior-trained collection of known poses to find an exemplar pose that represents an observed pose of the target;

rasterizing a model of the target into a synthesized depth image having a rasterized pose;

adjusting the rasterized pose of the model into a model-fitting pose based, at least in part, on differences between the observed depth image and the synthesized depth image; and

selecting the exemplar pose or the model-fitting pose.

6. The method of claim 5 , further comprising analyzing the observed depth image with a hand-identifying algorithm configured to identify hands on the target; and

biasing selection of the exemplar pose or the model-fitting pose toward a pose that more closely places hands in a same location as the hand-identifying algorithm.

7. The method of claim 5 , further comprising body scanning a scene of the observed depth image to identify the target.

8. The method of claim 5 , further comprising removing non-target background information from the observed depth image.

9. The method of claim 8 , where removing non-target background information from the observed depth image includes removing depth image information outside of a three-dimensional buffer surrounding the target.

10. The method of claim 8 , where removing non-target background information from the observed depth image includes removing depth image information outside of a sphere surrounding the target.

11. The method of claim 5 , where the source includes a depth camera.

12. The method of claim 5 , where the source includes stereo cameras.

13. The method of claim 5 , where analyzing the observed depth image with a prior-trained collection of known poses includes applying to the observed depth image one or more decision trees trained from the prior-trained collection of known poses.

14. The method of claim 13 , where a terminal node of a decision tree yields a best-guess of a body part for a pixel and a confidence that the best-guess is correct.

15. The method of claim 14 , further comprising locating each joint position of the exemplar pose based, at least in part, on the best-guess of the body part for each pixel.

16. The method of claim 15 , further comprising assigning a confidence to each joint position based, at least in part, on individual confidences for each pixel.

17. The method of claim 5 , further comprising assessing a confidence of the model-fitting pose based on a comparison of the model-fitting pose and the observed pose.

18. The method of claim 5 , where selecting the exemplar pose or the model-fitting pose includes selecting the exemplar pose if a confidence in the exemplar pose is higher than or equal to a confidence in the model-fitting pose, and selecting the model-fitting pose if the confidence in the model-fitting pose is higher than the confidence in the exemplar pose.

19. The method of claim 5 , where adjusting the rasterized pose of the model into the model-fitting pose includes applying one or more forces to force-receiving locations of the model and allowing the model to move responsive to such forces.

20. A computing system, comprising:

a source configured to capture depth information;

a logic subsystem operatively connected to the source; and

a data-holding subsystem holding instructions executable by the logic subsystem to:

receive an observed depth image of a target from a source;

analyze the observed depth image with a prior-trained collection of known poses to find an exemplar pose that represents an observed pose of the target;

rasterize a model of the target into a synthesized depth image having a rasterized pose;

adjust the rasterized pose of the model into a model-fitting pose based, at least in part, on differences between the observed depth image and the synthesized depth image; and

select the exemplar pose or the model-fitting pose.