SCENARIO-SPECIFIC BODY-PART TRACKING
First Claim
1. A computing system, comprising:
- a camera input subsystem to receive depth-camera recordings;
one or more memory devices holding instructions to retrain a general-purpose body part tracker;
one or more processors configured to execute the instructions to;
receive via the camera input subsystem, a set of different instances of scenario data, each instance of scenario data including one or more depth images representing a depth-camera recording of a human training-subject performing an action specific to a particular scenario;
iterate over the set of different instances of scenario data with the general-purpose body-part tracker, the general-purpose body-part tracker previously trained using supervised machine learning to identify one or more general-purpose parameters to be used by the general-purpose body-part tracker to track a human subject;
identify one or more special-purpose parameters to be selectively used to augment or replace the one or more general-purpose parameters if the general-purpose body-part tracker is used to track a human subject performing the action specific to the particular scenario; and
associate the one or more special-purpose parameters with a scenario identifier that identifies the particular scenario for which the special-purpose parameters are to be selectively used by the general-purpose body-part tracker to track a human subject.
2 Assignments
0 Petitions
Accused Products
Abstract
A human subject is tracked within a scene of an observed depth image supplied to a general-purpose body-part tracker. The general-purpose body-part tracker is retrained for a specific scenario. The general-purpose body-part tracker was previously trained using supervised machine learning to identify one or more general-purpose parameters to be used by the general-purpose body-part tracker to track a human subject. During a retraining phase, scenario data is received that represents a human training-subject performing an action specific to a particular scenario. One or more special-purpose parameters are identified from the processed scenario data. The special-purpose parameters are selectively used to augment or replace one or more general-purpose parameters if the general-purpose body-part tracker is used to track a human subject performing the action specific to the particular scenario.
-
Citations
20 Claims
-
1. A computing system, comprising:
-
a camera input subsystem to receive depth-camera recordings; one or more memory devices holding instructions to retrain a general-purpose body part tracker; one or more processors configured to execute the instructions to; receive via the camera input subsystem, a set of different instances of scenario data, each instance of scenario data including one or more depth images representing a depth-camera recording of a human training-subject performing an action specific to a particular scenario; iterate over the set of different instances of scenario data with the general-purpose body-part tracker, the general-purpose body-part tracker previously trained using supervised machine learning to identify one or more general-purpose parameters to be used by the general-purpose body-part tracker to track a human subject; identify one or more special-purpose parameters to be selectively used to augment or replace the one or more general-purpose parameters if the general-purpose body-part tracker is used to track a human subject performing the action specific to the particular scenario; and associate the one or more special-purpose parameters with a scenario identifier that identifies the particular scenario for which the special-purpose parameters are to be selectively used by the general-purpose body-part tracker to track a human subject. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
-
-
11. A method of retraining a body-part tracker, comprising:
-
receiving, from a currently-active application program, an indication of a particular scenario; receiving a set of different instances of scenario data, each instance of scenario data including one or more depth images of a human subject performing an action specific to the particular scenario while the currently-active application program is running; iterating over the set of different instances of scenario data with a general-purpose body-part tracker, the general-purpose body-part tracker previously trained using supervised machine learning to identify one or more general-purpose parameters to be used by the general-purpose body-part tracker to track a human subject; and identifying one or more special-purpose parameters to be subsequently used by the currently active application program to augment or replace the one or more general-purpose parameters, the one or more special-purpose parameters based on iteration over the set of different instances of scenario data received while the currently active application program was running. - View Dependent Claims (12, 13, 14, 15, 16, 17)
-
-
18. A computing system for tracking a human subject, comprising:
a computing device configured to; receive via a camera input subsystem of the computing device, input data including a depth image representing a depth-camera recording of a human subject performing an action while a currently-active application program is running; receive a scenario identifier from the currently-active application program indicated as being applicable to the input data; retrieve one or more special-purpose parameters associated with the scenario identifier from a data store; and analyze the input data with a general-purpose body-part tracker using the one or more special-purpose parameters that augment or replace one or more general-purpose parameters to identify a body model representing the human subject performing the action for use by the currently-active application program; the general-purpose body-part tracker previously trained using unsupervised machine learning to identify the one or more special-purpose parameters after being initially trained using supervised machine learning to identify the one or more general-purpose parameters. - View Dependent Claims (19, 20)
Specification