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.

Abstract: A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the subject as a model including a plurality of shapes.

Abstract: A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the subject as a model including a plurality of shapes.

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.

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.

Abstract: A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the subject as a model including a plurality of shapes.

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.

Abstract: A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the subject as a model including a plurality of shapes.

Abstract: A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the human subject as a body model including a plurality of shapes.

Abstract: A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the human subject as a body model including a plurality of shapes.

Abstract: A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the human subject as a body model including a plurality of shapes.

Abstract: A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the human subject as a body model including a plurality of shapes.

Abstract: A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the human subject as a body model including a plurality of shapes.

Abstract: A method of tracking a target includes receiving from a source an observed depth image of a scene including the target. Each pixel of the observed depth image is labeled as either a foreground pixel belonging to the target or a background pixel not belonging to the target. Each foreground pixel is labeled with body part information indicating a likelihood that that foreground pixel belongs to one or more body parts of the target. The target is modeled with a skeleton including a plurality of skeletal points, each skeletal point including a three dimensional position derived from body part information of one or more foreground pixels.

Abstract: A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the human subject as a body model including a plurality of shapes.

Abstract: A method is provided for using a wireless controller to interact with a user interface presented on a display. The method includes receiving an audio signal and a position signal from the wireless controller. The audio signal is based on an audio input applied to the wireless controller, while the position signal is based on a position input applied to the wireless controller. The method includes selecting a user interface item displayed on the display, based on the audio signal and the position signal. One or more position signals from the wireless controller may also be received and processed to cause navigation of the user interface to highlight a user interface item for selection.

Abstract: A method of tracking a target includes receiving from a source an observed depth image of a scene including the target. Each pixel of the observed depth image is labeled as either a foreground pixel belonging to the target or a background pixel not belonging to the target. Each foreground pixel is labeled with body part information indicating a likelihood that that foreground pixel belongs to one or more body parts of the target. The target is modeled with a skeleton including a plurality of skeletal points, each skeletal point including a three dimensional position derived from body part information of one or more foreground pixels.