Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may include a human target that may have, for example, a portion thereof non-visible or occluded. For example, a user may be turned such that a body part may not be visible to the device, may have one or more body parts partially outside a field of view of the device, may have a body part or a portion of a body part behind another body part or object, or the like such that the human target associated with the user may also have a portion body part or a body part non-visible or occluded in the depth image. A position or location of the non-visible or occluded portion or body part of the human target associated with the user may then be estimated.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. The image may then be processed. For example, the image may be downsampled, a shadow, noise, and/or a missing potion in the image may be determined, pixels in the image that may be outside a range defined by a capture device associated with the image may be determined, a portion of the image associated with a floor may be detected. Additionally, a target in the image may be determined and scanned. A refined image may then be rendered based on the processed image. The refined image may then be processed to, for example, track a user.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. The image may then be processed. For example, the image may be downsampled, a shadow, noise, and/or a missing potion in the image may be determined, pixels in the image that may be outside a range defined by a capture device associated with the image may be determined, a portion of the image associated with a floor may be detected. Additionally, a target in the image may be determined and scanned. A refined image may then be rendered based on the processed image. The refined image may then be processed to, for example, track a user.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may include a human target that may have, for example, a portion thereof non-visible or occluded. For example, a user may be turned such that a body part may not be visible to the device, may have one or more body parts partially outside a field of view of the device, may have a body part or a portion of a body part behind another body part or object, or the like such that the human target associated with the user may also have a portion body part or a body part non-visible or occluded in the depth image. A position or location of the non-visible or occluded portion or body part of the human target associated with the user may then be estimated.