The technology disclosed relates to relates to providing command input to a machine under control. It further relates to gesturally interacting with the machine. The technology disclosed also relates to providing monitoring information about a process under control. The technology disclosed further relates to providing biometric information about an individual. The technology disclosed yet further relates to providing abstract features information (pose, grab strength, pinch strength, confidence, and so forth) about an individual.

The technology disclosed relates to relates to providing command input to a machine under control. It further relates to gesturally interacting with the machine. The technology disclosed also relates to providing monitoring information about a process under control. The technology disclosed further relates to providing biometric information about an individual. The technology disclosed yet further relates to providing abstract features information (pose, grab strength, pinch strength, confidence, and so forth) about an individual.

A compact, low cost vcsel projector for high performance stereodepth camera is disclosed. An example apparatus includes an array of vertical cavity surface emitting lasers (VCSELs); an array of micro-lenses coupled to the array of VCSELs, centerlines of ones of the micro-lenses offset relative to centerlines of respective ones of the VCSELs; and a projection lens coupled to the array of micro-lenses.

A compact, low cost vcsel projector for high performance stereodepth camera is disclosed. An example apparatus includes an array of vertical cavity surface emitting lasers (VCSELs); an array of micro-lenses coupled to the array of VCSELs, centerlines of ones of the micro-lenses offset relative to centerlines of respective ones of the VCSELs; and a projection lens coupled to the array of micro-lenses.

A method includes: accessing a floorplan representing the floorspace; and extracting from the floorplan a set of floorplan features representing areas of interest in the floorspace. The method also includes, calculating a set of target locations relative to the floorplan that, when occupied by the set of sensor blocks: locate the areas of interest in the floorspace within fields of view of the set sensor blocks; and yield a minimum overlap in fields of view of adjacent sensor blocks in the set of sensor blocks. The method further includes, for each sensor block in the sensor blocks installed over the floorspace: receiving, from the sensor block, an image of the floorspace; based on overlaps in the image with images from other sensor blocks in sensor blocks, estimating an installed location of the sensor block; and mapping the sensor block to a target location in the set of target locations.

A method includes: accessing a floorplan representing the floorspace; and extracting from the floorplan a set of floorplan features representing areas of interest in the floorspace. The method also includes, calculating a set of target locations relative to the floorplan that, when occupied by the set of sensor blocks: locate the areas of interest in the floorspace within fields of view of the set sensor blocks; and yield a minimum overlap in fields of view of adjacent sensor blocks in the set of sensor blocks. The method further includes, for each sensor block in the sensor blocks installed over the floorspace: receiving, from the sensor block, an image of the floorspace; based on overlaps in the image with images from other sensor blocks in sensor blocks, estimating an installed location of the sensor block; and mapping the sensor block to a target location in the set of target locations.

Systems and techniques are provided for tracking puts and takes of inventory items by subjects in an area of real space. A plurality of cameras with overlapping fields of view produce respective sequences of images of corresponding fields of view in the real space. In one embodiment, the system includes first image processors, including subject image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The first image processors process images to identify subjects represented in the images in the corresponding sequences of images. The system includes second image processors, including background image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The second image processors mask the identified subjects to generate masked images. Following this, the second image processors process the masked images to identify and classify background changes represented in the images in the corresponding sequences of images.

Systems and techniques are provided for tracking puts and takes of inventory items by subjects in an area of real space. A plurality of cameras with overlapping fields of view produce respective sequences of images of corresponding fields of view in the real space. In one embodiment, the system includes first image processors, including subject image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The first image processors process images to identify subjects represented in the images in the corresponding sequences of images. The system includes second image processors, including background image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The second image processors mask the identified subjects to generate masked images. Following this, the second image processors process the masked images to identify and classify background changes represented in the images in the corresponding sequences of images.

Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.

Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.