Scenario-specific body-part tracking

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.

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.