Abstract: The description relates to cameras, and camera calibration for enhancing user experiences. One example can receive a first image of a user at a first location relative to a camera. The first image can include the user's upper body but does not include the user from head to toe. The example can receive a second image of the user at a second location relative to a camera. The second image can include the user's upper body but does not include the user from head to toe. The example can estimate a distance of the second location from the first location relative to the camera and calibrate a height and tilt angle of the camera from the first image, the second image, and the estimated distance and without a full body image of the user.

Abstract: Techniques for improved camera calibration are disclosed. An image is analyzed to identify a first set of key points for an object. A virtual object is generated. The virtual object has a second set of key points. A reprojected version of the second set is fitted to the first set in 2D space until a fitting threshold is satisfied. To do so, a 3D alignment of the second set is generated in an attempt to fit (e.g., in 2D space) the second set to the first set. Another operation includes reprojecting the second set into 2D space. In response to comparing the reprojected second set to the first set, another operation includes determining whether a fitting error between those sets satisfies the fitting threshold. A specific 3D alignment of the second set is selected. The camera is calibrated based on resulting reprojection parameters.

Abstract: Techniques for inferring whether an event is occurring in 3D space based on 2D image data and for maintaining a camera's calibration are disclosed. An image of an environment is accessed. Input is received, where the input includes a 2D rule imposed against a ground plane. The 2D rule includes conditions indicative of an event. A bounding box is generated and encompasses a detected object. A point within the bounding box is projected from a 2D-space image plane of the image into 3D space to generate a 3D-space point. Based on the 3D-space point, a 3D-space ground contact point is generated. That 3D-space ground contact point is reprojected onto the ground plane of the image to generate a synthesized 2D ground contact point. A location of the synthesized 2D ground contact point is determined to satisfy the conditions.

Abstract: Techniques for inferring whether an event is occurring in 3D space based on 2D image data and for maintaining a camera's calibration are disclosed. An image of an environment is accessed. Input is received, where the input includes a 2D rule imposed against a ground plane. The 2D rule includes conditions indicative of an event. A bounding box is generated and encompasses a detected object. A point within the bounding box is projected from a 2D-space image plane of the image into 3D space to generate a 3D-space point. Based on the 3D-space point, a 3D-space ground contact point is generated. That 3D-space ground contact point is reprojected onto the ground plane of the image to generate a synthesized 2D ground contact point. A location of the synthesized 2D ground contact point is determined to satisfy the conditions.

Abstract: A method to predict a traversal-time interval for traversal of a service queue comprises receiving video of a region including the service queue, recognizing in the video, via machine vision, a plurality of persons awaiting service within the region, estimating an average crossing-time interval between successive crossings, by the plurality of persons, of a fixed boundary along the service queue, wherein such estimating is based on features of the service queue and of the one or more persons awaiting service, and returning an estimate of the traversal-time interval based on a count of the persons awaiting service and on the average crossing-time interval as estimated.

Abstract: Techniques for improved camera calibration are disclosed. An image is analyzed to identify a first set of key points for an object. A virtual object is generated. The virtual object has a second set of key points. A reprojected version of the second set is fitted to the first set in 2D space until a fitting threshold is satisfied. To do so, a 3D alignment of the second set is generated in an attempt to fit (e.g., in 2D space) the second set to the first set. Another operation includes reprojecting the second set into 2D space. In response to comparing the reprojected second set to the first set, another operation includes determining whether a fitting error between those sets satisfies the fitting threshold. A specific 3D alignment of the second set is selected. The camera is calibrated based on resulting reprojection parameters.

Abstract: Techniques for improved camera calibration are disclosed. An image is analyzed to identify a first set of key points for an object. A virtual object is generated. The virtual object has a second set of key points. A reprojected version of the second set is fitted to the first set in 2D space until a fitting threshold is satisfied. To do so, a 3D alignment of the second set is generated in an attempt to fit (e.g., in 2D space) the second set to the first set. Another operation includes reprojecting the second set into 2D space. In response to comparing the reprojected second set to the first set, another operation includes determining whether a fitting error between those sets satisfies the fitting threshold. A specific 3D alignment of the second set is selected. The camera is calibrated based on resulting reprojection parameters.

Abstract: Improved techniques for determining an object's 3D orientation. An image is analyzed to identify a 2D object and a first set of key points. The first set defines a first polygon. A 3D virtual object is generated. This 3D virtual object has a second set of key points defining a second polygon representing an orientation of the 3D virtual object. The second polygon is rotated a selected number of times. For each rotation, each rotated polygon is reprojected into 2D space, and a matching score is determined between each reprojected polygon and the first polygon. A specific reprojected polygon is selected whose corresponding matching score is lowest. The orientation of the 3D virtual object is set to an orientation corresponding to the specific reprojected polygon. Based on the orientation of the 3D virtual object, an area of focus of the 2D object is determined.

Abstract: Techniques for inferring whether an event is occurring in 3D space based on 2D image data and for maintaining a camera's calibration are disclosed. An image of an environment is accessed. Input is received, where the input includes a 2D rule imposed against a ground plane. The 2D rule includes conditions indicative of an event. A bounding box is generated and encompasses a detected object. A point within the bounding box is projected from a 2D-space image plane of the image into 3D space to generate a 3D-space point. Based on the 3D-space point, a 3D-space ground contact point is generated. That 3D-space ground contact point is reprojected onto the ground plane of the image to generate a synthesized 2D ground contact point. A location of the synthesized 2D ground contact point is determined to satisfy the conditions.

Abstract: Improved techniques for determining an object's 3D orientation. An image is analyzed to identify a 2D object and a first set of key points. The first set defines a first polygon. A 3D virtual object is generated. This 3D virtual object has a second set of key points defining a second polygon representing an orientation of the 3D virtual object. The second polygon is rotated a selected number of times. For each rotation, each rotated polygon is reprojected into 2D space, and a matching score is determined between each reprojected polygon and the first polygon. A specific reprojected polygon is selected whose corresponding matching score is lowest. The orientation of the 3D virtual object is set to an orientation corresponding to the specific reprojected polygon. Based on the orientation of the 3D virtual object, an area of focus of the 2D object is determined.

Abstract: Techniques for improved camera calibration are disclosed. An image is analyzed to identify a first set of key points for an object. A virtual object is generated. The virtual object has a second set of key points. A reprojected version of the second set is fitted to the first set in 2D space until a fitting threshold is satisfied. To do so, a 3D alignment of the second set is generated in an attempt to fit (e.g., in 2D space) the second set to the first set. Another operation includes reprojecting the second set into 2D space. In response to comparing the reprojected second set to the first set, another operation includes determining whether a fitting error between those sets satisfies the fitting threshold. A specific 3D alignment of the second set is selected. The camera is calibrated based on resulting reprojection parameters.

Abstract: Improved techniques for determining an object's 3D orientation. An image is analyzed to identify a 2D object and a first set of key points. The first set defines a first polygon. A 3D virtual object is generated. This 3D virtual object has a second set of key points defining a second polygon representing an orientation of the 3D virtual object. The second polygon is rotated a selected number of times. For each rotation, each rotated polygon is reprojected into 2D space, and a matching score is determined between each reprojected polygon and the first polygon. A specific reprojected polygon is selected whose corresponding matching score is lowest. The orientation of the 3D virtual object is set to an orientation corresponding to the specific reprojected polygon. Based on the orientation of the 3D virtual object, an area of focus of the 2D object is determined.

Abstract: A machine learning engine may be used to identify items in a second item category that have a visual appearance similar to the visual appearance of a first item selected from a first item category. Image data and text data associated with a large number of items from different item categories may be processed and used by an association model created by a machine learning engine. The association model may extract item attributes from the image data and text data of the first item. The machine learning engine may determine weights for parameter types, and the weights may calibrate the influence of the respective parameter types on the search results. The association model may be deployed to identify items from different item categories that have a visual appearance similar to the first item. The association model may be updated over time by the machine learning engine as data correlations evolve.