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 sensor system creates a sequence of depth images that are used to detect and track motion of objects within range of the sensor system. A reference image is created and updated based on a moving average (or other function) of a set of depth images. A new depth images is compared to the reference image to create a motion image, which is an image file (or other data structure) with data representing motion. The new depth image is also used to update the reference image. The data in the motion image is grouped and associated with one or more objects being tracked. The tracking of the objects is updated by the grouped data in the motion image. The new positions of the objects are used to update an application.

Abstract: Systems and associated methods for processing textures in a graphical processing unit (GPU) are disclosed. Textures may be managed on a per region (e.g., tile) basis, which allows efficient use of texture memory. Moreover, very large textures may be used. Techniques provide for both texture streaming, as well as sparse textures. A GPU texture unit may be used to intelligently clamp LOD based on a shader specified value. The texture unit may provide feedback to the shader to allow the shader to react conditionally based on whether clamping was used, etc. Per region (e.g., per-tile) independent mipmap stacks may be used to allow very large textures.

Abstract: Allocation of memory registers for shaders by a processor is described herein. For each shader, registers are allocated based on the shader's level of complexity. Simpler shader instances are restricted to a smaller number of memory registers. More complex shader instances are allotted more registers. To do so, developers' high level shading level (HLSL) language includes template classes of shaders that can later be replaced by complex or simple versions of the shader. The HLSL is converted to bytecode that can be used to rasterize pixels on a computing device.

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: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may be determined and a model may be adjusted based on the location or position of the one or more extremities.

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: Techniques are provided for re-orienting a field of view of a depth camera having one or more sensors. The depth camera may have one or more sensors for generating a depth image and may also have an RGB camera. In some embodiments, the field of view is re-oriented based on the depth image. The position of the sensor(s) may be altered to change the field of view automatically based on an analysis of objects in the depth image. The re-orientation process may be repeated until a desired orientation of the sensor is determined. Input from the RGB camera might be used to validate a final orientation of the depth camera, but is not required to during the process of determining new possible orientation of the field of view.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may be determined and a model may be adjusted based on the location or position of the one or more extremities.

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 for controlling a computer system includes acquiring video of a subject, and obtaining from the video a time-resolved sequence of depth maps. A geometric model of the subject is fit to each depth map in the sequence and tracked into a subsequent depth map in the sequence. From the subsequent depth map, a background section is selected for exclusion. The background section is one that lacks coherent motion and is located more than a threshold distance from the coordinates of the geometric model tracked in. Then, a subsequent geometric model of the subject is fit to the depth map with the background section excluded.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A model may be adjusted based on a location or position of one or more extremities estimated or determined for a human target in the grid of voxels. The model may also be adjusted based on a default location or position of the model in a default pose such as a T-pose, a DaVinci pose, and/or a natural pose.

Abstract: A sensor system creates a sequence of depth images that are used to detect and track motion of objects within range of the sensor system. A reference image is created and updated based on a moving average (or other function) of a set of depth images. A new depth images is compared to the reference image to create a motion image, which is an image file (or other data structure) with data representing motion. The new depth image is also used to update the reference image. The data in the motion image is grouped and associated with one or more objects being tracked. The tracking of the objects is updated by the grouped data in the motion image. The new positions of the objects are used to update an application.

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 for controlling a computer system includes acquiring video of a subject, and obtaining from the video a time-resolved sequence of depth maps. A geometric model of the subject is fit to each depth map in the sequence and tracked into a subsequent depth map in the sequence. From the subsequent depth map, a background section is selected for exclusion. The background section is one that lacks coherent motion and is located more than a threshold distance from the coordinates of the geometric model tracked in. Then, a subsequent geometric model of the subject is fit to the depth map with the background section excluded.

Abstract: A method for controlling a computer system includes acquiring video of a subject, and obtaining from the video a time-resolved sequence of depth maps. An area targeting motion is selected from each depth map in the sequence. Then, a section of the depth map bounded by the area and lying in front of a plane is selected. This section of the depth map is used for fitting a geometric model of the subject.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A model may be adjusted based on a location or position of one or more extremities estimated or determined for a human target in the grid of voxels. The model may also be adjusted based on a default location or position of the model in a default pose such as a T-pose, a DaVinci pose, and/or a natural pose.

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 sensor system creates a sequence of depth images that are used to detect and track motion of objects within range of the sensor system. A reference image is created and updated based on a moving average (or other function) of a set of depth images. A new depth images is compared to the reference image to create a motion image, which is an image file (or other data structure) with data representing motion. The new depth image is also used to update the reference image. The data in the motion image is grouped and associated with one or more objects being tracked. The tracking of the objects is updated by the grouped data in the motion image. The new positions of the objects are used to update an application.

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.