Abstract: An edge computing system is deployed at a physical location and receives an input from one or more image/video sensing mechanisms. The edge computing system executes artificial intelligence image/video processing modules on the received image/video streams and generates metrics by performing spatial analysis on the images/video stream. The metrics are provided to a multi-tenant service computing system where additional artificial intelligence (AI) modules are executed on the metrics to execute perception analytics. Client applications can then be run on the output of the AI modules in the multi-tenant service computing system.

Abstract: An edge computing system is deployed at a physical location and receives an input from one or more image/video sensing mechanisms. The edge computing system executes artificial intelligence image/video processing modules on the received image/video streams and generates metrics by performing spatial analysis on the images/video stream. The metrics are provided to a multi-tenant service computing system where additional artificial intelligence (AI) modules are executed on the metrics to execute perception analytics. Client applications can then be run on the output of the AI modules in the multi-tenant service computing system.

Abstract: An edge computing system is deployed at a physical location and receives an input from one or more image/video sensing mechanisms. The edge computing system executes artificial intelligence image/video processing modules on the received image/video streams and generates metrics by performing spatial analysis on the images/video stream. The metrics are provided to a multi-tenant service computing system where additional artificial intelligence (AI) modules are executed on the metrics to execute perception analytics. Client applications can then be run on the output of the AI modules in the multi-tenant service computing system.

Abstract: A system determines relative camera locations based on one or more persons appearing in one or more acquired images. In some instances, the system receives a plurality of images from a plurality of cameras, and identifies a skeletal structure of a person in each of the plurality of images. The system then determines localization parameters for the plurality of cameras based on intrinsic physical characteristics of the plurality of cameras and the identified skeletal structure. The localization parameters may include the positional and/or orientation parameter values for one or more of the cameras. The system then triangulates positions of the identified skeletal structure represented by the plurality of pixels. The system then outputs relative location information of the plurality of cameras based on the localization parameters.

Abstract: Methods, apparatus and systems are employed to generate a dynamic content, for example, a looping video using an application operating on a device including a graphical user interface presented in a display. In some embodiments, a video stream including audio is acquired by employing the graphical user interface to operate an application displayed in the graphical user interface and a continuous loop is generated from the acquired video stream, the application automatically limiting a length of the continuous loop to a maximum amount of time pre-determined by the application.

Abstract: Methods, apparatus and systems are employed to generate a dynamic image, for example, an animated image in which a selected region of the image includes action. In some embodiments, the dynamic image is generated from a stream of images which are stabilized.