Unifai Holdings Limited

Apical Limited

Arm Limited

Michael Tusch

Ilya Romanenko

Andrew Jackson

John Dunne

The field of the invention relates to computer vision systems and methods providing real time data analytics on detected people or objects in the home environment or other environments. It is based on an embedded engine that analyses an image from a raw sensor and virtualised the image into a digital representation enabling a digital understanding of the environment while guarantying privacy. It comprises multiple image processing blocks and embedded firmware.

Computer vision system

A computer-vision-based monitoring system for a road vehicle, including: (i) a server located external to the road vehicle and forming part of a distributed computing infrastructure; (ii) a first camera positioned in, or configured to be attached to, the road vehicle and configured to capture an image of the environment external to the road vehicle; (iii) a computer vision sub-system connected to the first camera, in which the computer vision sub-system is configured to be locatable in the road vehicle, and forms or includes at least some of an edge layer of the distributed computing infrastructure; (iv) a person tracking sub-system that is connected to, or is part of, the computer vision sub-system, the person tracking sub-system having been trained using machine learning, wherein the person tracking sub-system is configured to use its training using the machine learning to analyse a trajectory of a person approaching the road vehicle.

A computer vision-based monitoring system for counting the number of occupants in a road vehicle, including a server located external to the road vehicle and forming part of a distributed computing infrastructure; a first camera positioned in, or configured to be attached to, the road vehicle and configured to capture an image of the environment external to the road vehicle; a second camera positioned in, or configured to be attached to, the road vehicle and configured to capture an image of one or more occupants in the road vehicle; a computer vision sub-system connected to the cameras, configured to be locatable in the road vehicle, and includes an edge layer of the distributed computing infrastructure; a vehicle occupant counting sub-system trained using machine learning, wherein the sub-system is configured to use its training to count the number of occupants in the road vehicle.

A computer-vision system or engine that (a) generates from a pixel stream a digital representation of a person and (b) determines attributes or characteristics of the person from that digital representation and (c) based on those attributes or characteristics, outputs data to a cloud-based analytics system that enables that analytics system to identify and also to authenticate the person. The attributes or characteristics of the person include their pose, and the system or engine analyses that pose to extract a facial image from a video stream that is the best facial image for use by the cloud-based analytics system to identify and authenticate the person. The computer-vision system or engine outputs the facial image to the cloud-based analytics system, but does not output the full-frame real-time video to the cloud-based analytics system.

Computer vision systems

An image processing method is provided. The method includes acquiring a video. The method includes using an object detection engine to detect a person in the video. The object detection engine is integrated with an image signal processing pipeline. The method includes transmitting the video over a network. The method includes determining that the detected person has moved less than a pre-set distance. The method includes, responsive to the determining, pausing transmission of the video. An embedded image processor including an object detection engine is also provided.

Image processing method

A method of forming a control parameter dependent on ambient light. The method comprises the steps of acquiring light values from an ambient light sensor and acquiring positional status values from a positional status sensor. The control parameter depends on the light values and is filtered in dependence on the positional status values.

Method and device for ambient light estimation

In a method of enhancing the appearance of a digital image on an electronic display, Ambient Light Sensor (ALS) and Screen Brightness (SB) inputs are combined with a calibration inputto generate a first strength parameter, including an alpha component that is dependent only upon the properties of the display. Then a further, content-based inputis also combined to generate a second strength parameter, including a beta component that is dependent only upon a content type, or a content quality, or both.

Method and apparatus for controlling a display

A method to frame a video stream is provided. A video stream is captured, then motion of the camera, object presence in the video stream, and position of the object are detected. A framed video stream is generated using a framing in dependence on the motion of the camera and the position of the object. A system for framing a video stream is also provided, wherein the system comprises a camera and a processing unit, and the processing unit comprises at least one processor and at least on memory including computer program instructions to execute a method to frame a video stream.

Method of generating a framed video system

A method of controlling in a display device screen brightness and strength of dynamic range compression of display data in dependence on ambient light level. Ambient light level values are required from one or more ambient light sensors. The ambient light level values are averaged using a first method to obtain a first average value and using a second, different method to obtain a second average value. The first average value is used for controlling the screen brightness and the second average value is used for controlling the strength of the dynamic range compression.

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London, United Kingdom

Michael Tusch