A method for processing data includes receiving a depth map of a scene containing a humanoid form. The depth map is processed so as to identify three-dimensional (3D) connected components in the scene, each connected component including a set of the pixels that are mutually adjacent and have mutually-adjacent depth values. Separate, first and second connected components are identified as both belonging to the humanoid form, and a representation of the humanoid form is generated including both of the first and second connected components.

Analysis of three-dimensional scenes

Use of a 3D environment model in gameplay is described. In an embodiment, a mobile depth camera is used to capture a series of depth images as it is moved around and a dense 3D model of the environment is generated from this series of depth images. This dense 3D model is incorporated within an interactive application, such as a game. The mobile depth camera is then placed in a static position for an interactive phase, which in some examples is gameplay, and the system detects motion of a user within a part of the environment from a second series of depth images captured by the camera. This motion provides a user input to the interactive application, such as a game. In further embodiments, automatic recognition and identification of objects within the 3D model may be performed and these identified objects then change the way that the interactive application operates.

Using a three-dimensional environment model in gameplay

Techniques for human body pose estimation are disclosed herein. Depth map images from a depth camera may be processed to calculate a probability that each pixel of the depth map is associated with one or more segments or body parts of a body. Body parts may then be constructed of the pixels and processed to define joints or nodes of those body parts. The nodes or joints may be provided to a system which may construct a model of the body from the various nodes or joints.

Human body pose estimation

A projector is disclosed for use in a 3-D imaging device. The projector includes a light source formed of a vertical-cavity surface-emitting laser, or VCSEL array. The VCSEL array provides a light source for illuminating a capture area. Light from the VCSEL array is reflected off of objects in the capture area and received within a sensing device such as a 3-D camera. The projector may further include a collimating lens array for focusing the light emitted from each VCSEL in the array, as well as a DOE for patterning the light from the collimating lens array to enable the sensing device to generate a 3-D image of the objects in the capture area.

Depth projector system with integrated vcsel array

Mobile camera localization using depth maps is described for robotics, immersive gaming, augmented reality and other applications. In an embodiment a mobile depth camera is tracked in an environment at the same time as a 3D model of the environment is formed using the sensed depth data. In an embodiment, when camera tracking fails, this is detected and the camera is relocalized either by using previously gathered keyframes or in other ways. In an embodiment, loop closures are detected in which the mobile camera revisits a location, by comparing features of a current depth map with the 3D model in real time. In embodiments the detected loop closures are used to improve the consistency and accuracy of the 3D model of the environment.

Mobile camera localization using depth maps

Technology for testing a target recognition, analysis, and tracking system is provided. A searchable repository of recorded and synthesized depth clips and associated ground truth tracking data is provided. Data in the repository is used by one or more processing devices each including at least one instance of a target recognition, analysis, and tracking pipeline to analyze performance of the tracking pipeline. An analysis engine provides at least a subset of the searchable set responsive to a request to test the pipeline and receives tracking data output from the pipeline on the at least subset of the searchable set. A report generator outputs an analysis of the tracking data relative to the ground truth in the at least subset to provide an output of the error relative to the ground truth.

Validation analysis of human target

A depth image of a scene may be observed or captured by a capture device. The depth image may include a human target and an environment. One or more pixels of the depth image may be analyzed to determine whether the pixels in the depth image are associated with the environment of the depth image. The one or more pixels associated with the environment may then be discarded to isolate the human target and the depth image with the isolated human target may be processed.

Environment and/or Target Segmentation

A method of tracking a target includes receiving an observed depth image of the target from a source and analyzing the observed depth image with a prior-trained collection of known poses to find an exemplar pose that represents an observed pose of the target The method further includes rasterizing a model of the target into a synthesized depth image having a rasterized pose and adjusting the rasterized pose of the model into a model-fitting pose based, at least in part, on differences between the observed depth image and the synthesized depth image Either the exemplar pose or the model-fitting pose is then selected to represent the target

Visual target tracking using model fitting and exemplar

A virtual skeleton includes a plurality of joints and provides a machine readable representation of a human subject observed with a sensor such as a depth camera. A gesture detection module is trained via machine learning to identify one or more features of a virtual skeleton and indicate if the feature(s) collectively indicate a particular gesture.

Machine learning gesture detection

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.

Charles Claudius Marais

Papers

Validation analysis of human target

Environment and/or Target Segmentation

Visual target tracking using model fitting and exemplar

Machine learning gesture detection

Systems And Methods For Processing An Image For Target Tracking