Method and apparatus for spline-based trajectory classification, gesture detection and localization

• US 20040001113A1
• Filed: 06/28/2002
• Published: 01/01/2004
• Est. Priority Date: 06/28/2002
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Zipperer, John, Martins, Fernando C.M.
• Application Number:
  US10/185,579
• Publication Number:
  US 20040001113A1
• Time in Patent Office: Days
• US Class Current: 345/853
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883

Abstract

A gesture classification method includes receiving position data. A detected spline is generated based on the position data. A normalization scheme is applied to the detected spline to generate a normalized spline. A goodness value is determined by comparing the normalized spline with gesture splines representing gestures stored in a gesture database.

Claims

1. A gesture classification method, comprising:

receiving position data;

generating a detected spline based on the position data;

applying a normalization scheme to the detected spline to generate a normalized spline; and

determining a goodness value by comparing the normalized spline with gesture splines representing gestures stored in a gesture database.

2. The method according to claim 1, wherein the normalization scheme includes determining and scaling a convex hull of the detected spline.

3. The method according to claim 1, wherein the normalization scheme includes determining at least one moment of the detected spline.

4. The method according to claim 1, wherein the normalization scheme includes implementing a translation invariance scheme.

5. The method according to claim 1, wherein the normalized spline includes a normalized basis function and a predetermined number of normalized control points.

6. The method according to claim 1, wherein each of the gesture splines include a gesture basis function and gesture control points.

7. The method according to claim 1, wherein the goodness value is determined after calculating an L2 norm of the distances between respective normalized control points and gesture control points of the gesture splines.

8. The method according to claim 7, wherein a matching gesture is returned if the goodness value is above a predetermined threshold.

9. The method according to claim 1, wherein the spline is a B-spline.

10. A gesture detection method, comprising:

receiving a set of position data;

determining a start data point and a stop data point of the set of position data;

testing the position data between the start data point and the stop data point via comparison with data representing predetermined gestures; and

locating a gesture within the set of position data based on the testing.

11. The method of claim 10, wherein the testing includes calculating a B-spline for the position data between the start data point and the stop data point.

12. The method of claim 11, further including determining an L2 norm based on a difference between the B-spline for the position data and a predetermined B-spline for a predetermined gesture.

13. The method of claim 12, further including determining a closest matching gesture determined from a set of the positional data between the start data point and the stop data point, wherein the set includes determined positional data having a sufficient number of data points to represent all allowable gestures.

14. A gesture recognition device, comprising:

a spline generating device to generate a spline based on a set of positional data;

a normalization device to normalize the spline; and

a goodness determination device to determine a goodness value based on how closely the spline correlates with a spline representing a gesture stored in a gesture vocabulary, and return the gesture if the goodness value exceeds a threshold value.

15. The gesture recognition device of claim 14, wherein the spline is a B-spline.

16. The gesture recognition device of claim 14, wherein the normalization device includes a convex hull determination and scale device.

17. The gesture recognition device of claim 14, wherein the normalization device includes a moment calculation device.

18. The gesture recognition device of claim 14, wherein the normalization device includes a translation device.

19. The gesture recognition device of claim 14, further including a gesture vocabulary device to store the gesture vocabulary.

20. A gesture recognition system, comprising:

a raw data acquisition device to acquire a set of positional data;

a spline generating device to generate a spline based on the set of positional data;

a normalization device to normalize the spline; and

a goodness determination device to determine a goodness value based on how closely the spline correlates with a spline representing a gesture stored in a gesture vocabulary, and return the gesture if the goodness value exceeds a threshold value.

21. The gesture recognition system of claim 20, wherein the spline is a B-spline.

22. The gesture recognition system of claim 20, wherein the normalization device includes a convex hull determination and scale device.

23. The gesture recognition system of claim 20, wherein the normalization device includes a moment calculation device.

24. The gesture recognition system of claim 20, wherein the normalization device includes a translation device.

25. The gesture recognition system of claim 20, further including a gesture vocabulary device to store the gesture vocabulary.

26. The gesture recognition system of claim 20, wherein the raw data acquisition device includes a mouse.

27. The gesture recognition system of claim 20, wherein the raw data acquisition device includes an I/O device.

28. The gesture recognition system of claim 20, wherein the raw data acquisition device includes a touchpad.

29. The gesture recognition system of claim 20, wherein the raw data acquisition device includes a videocamera.

30. An article comprising:

a storage medium having stored thereon first instructions that when executed by a machine result in the following;

receiving position data;

generating a detected spline based on the position data;

applying a normalization scheme to the detected spline to generate a normalized spline; and

determining a goodness value by comparing the normalized spline with gesture splines representing gestures stored in a gesture database.

31. The article according to claim 30, wherein the normalization scheme includes determining and scaling a convex hull of the detected spline.

32. The article according to claim 30, wherein the normalization process includes determining at least one moment of the detected spline.

33. The article according to claim 30, wherein the normalization process includes implementing a translation invariance scheme.

34. The article according to claim 30, wherein the normalized spline includes a normalized basis function and a predetermined number of normalized control points.

35. The article according to claim 30, wherein each of the gesture splines include a gesture basis function and gesture control points.

36. The article according to claim 30, wherein the goodness value is determined after calculating an L2 norm of the distances between respective normalized control points and gesture control points of the gesture splines.

37. The article according to claim 30, wherein a matching gesture is outputted if the goodness value is above a predetermined threshold.

38. An article comprising:

a storage medium having stored thereon first instructions that when executed by a machine result in the following;

receiving a set of position data;

determining a start data point and a stop data point of the set of position data;

testing the position data between the start data point and the stop data point via comparison with data representing predetermined gestures; and

locating a gesture within the set of position data based on the testing.

39. The article of claim 38, wherein the testing includes calculating a B-spline for the position data between the start data point and the stop data point.

40. The article of claim 38, further including determining an L2 norm based on a difference between the B-spline for the position data and a B-spline for a predetermined gesture.

41. The article of claim 38, further including determining a closest matching gesture determined from a set of the positional data between the start data point and the stop data point, wherein the set includes determined positional data having a sufficient number of data points to represent all allowable gestures.