US 10,304,002 B2Granted

Depth-based feature systems for classification applications

AssigneeYouSpace, Inc.

InventorRalph Brunner

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Abstract

Human Computer Interfaces (HCI) may allow a user to interact with a computer via a variety of mechanisms, such as hand, head, and body gestures. Various of the disclosed embodiments allow information captured from a depth camera on an HCI system to be used to recognize such gestures. Particularly, the HCI system's depth sensor may capture depth frames of the user's movements over time. To discern gestures from these movements, the system may group portions of the user's anatomy represented by the depth data into classes. “Features” which reflect distinguishing features of the user's anatomy may be used to accomplish this classification. Some embodiments provide improved systems and methods for generating and/or selecting these features. Features prepared by various of the disclosed embodiments may be less susceptible to overfitting training data and may more quickly distinguish portions of the user's anatomy.

Claims20

Dependent claims

A computer system configured to recognize a user gesture from depth data, the computer system comprising:

at least one processor;
at least one memory comprising instructions configured to cause the computer system to perform a method comprising:
receiving depth data captured while a user performed a gesture;
applying a template to the depth data to generate a plurality of features;
and
identifying the gesture based upon the plurality of features, wherein
the template was generated, at least in part, by:
determining a Gaussian function;
sampling the Gaussian function to generate a first distribution;
and
generating a second distribution by iteratively sampling the first distribution and selecting points of the first distribution using a metric, wherein selecting points of the first distribution using a metric comprises determining a minimum of a plurality of Sub_Metric values, each Sub_Metric value determined using the formula
Sub_Metric
⁢
⁢
(
P
,
W
)
=
e
-
(
P
x
+
W
x
2
)
2
-
(
P
y
+
W
y
2
)
2
⁢
(
P
x
-
W
x
)
2
+
(
P
y
-
W
y
)
2
,
wherein P is a point from the first distribution,
wherein W is a point from the second distribution,
wherein PX is the X coordinate of the point P from the first distribution,
wherein PY is the Y coordinate of the point P from the first distribution,
wherein WX is the X coordinate of the point W from the second distribution, and
wherein WY is the Y coordinate of the point W from the second distribution.

63 more elements

A non-transitory computer-readable medium comprising instructions configured to cause a computer system to perform a method to recognize a user gesture from depth data, the method comprising:

receiving depth data captured while a user performed a gesture;
applying a template to the depth data to generate a plurality of features;
and
identifying the gesture based upon the plurality of features, wherein
the template was generated, at least in part, by:
determining a Gaussian function;
sampling the Gaussian function to generate a first distribution;
and
generating a second distribution by iteratively sampling the first distribution and selecting points of the first distribution using a metric, wherein selecting points of the first distribution using a metric comprises determining a minimum of a plurality of Sub_Metric values, each Sub_Metric value determined using the formula
Sub_Metric
⁢
⁢
(
P
,
W
)
=
e
-
(
P
x
+
W
x
2
)
2
-
(
P
y
+
W
y
2
)
2
⁢
(
P
x
-
W
x
)
2
+
(
P
y
-
W
y
)
2
,
wherein P is a point from the first distribution,
wherein W is a point from the second distribution,
wherein PX is the X coordinate of the point P from the first distribution,
wherein PY is the Y coordinate of the point P from the first distribution,
wherein WX is the X coordinate of the point W from the second distribution, and
wherein WY is the Y coordinate of the point W from the second distribution.

61 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/018,303Filed 8 Feb 2016
Published: US 2017/0228647 A110 Aug 2017
Granted: 28 May 2019
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06N 5/025
Knowledge engineering; Knowledge acquisition: extracting rules from data
G06F 3/017
Input arrangements or combined input and output arrangements for interaction between user and computer: gesture based interaction, e.g. based on a…
G06F 3/0304
Arrangements for converting the position or the displacement of a member into a coded form: detection arrangements using opto-electronic means
G06N 7/01
Computing arrangements based on specific mathematical models: probabilistic graphical models, e.g. probabilistic networks