Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.

https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Du%20-%20Automatic%20License%20Plate%20Recognition%20-%202013.pdf

Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

Computer-aided detection and classification of microcalcifications in mammograms: a survey

https://visual.ic.uff.br/proeng/artigos/bom3.pdf

Approaches for automated detection and classification of masses in mammograms

Methods can include using a doorbell to wirelessly communicate with a remotely located computing device. Doorbells can include a speaker, a microphone, a camera, and a button to sound a chime. A doorbell shipping mode can detect whether the doorbell is electrically coupled to an external power source. Methods can include entering a setup mode or a standby mode in response to detecting electricity from the external power source.

Doorbell communication systems and methods

Disclosed are methods, systems, computer readable media and other implementations, including a method to calibrate a camera that includes capturing by the camera a frame of a scene, identifying features appearing in the captured frame, the features associated with pre-determined values representative of physical attributes of one or more objects, and determining parameters of the camera based on the identified features appearing in the captured frame and the pre-determined values associated with the identified features.

Camera calibration using feature identification

Techniques are described for tracking moving objects using a plurality of security cameras. Multiple cameras may capture frames that contain images of a moving object. These images may be processed by the cameras to create metadata associated with the images of the objects. Frames of each camera's video feed and metadata may be transmitted to a host computer system. The host computer system may use the metadata received from each camera to determine whether the moving objects imaged by the cameras represent the same moving object. Based upon properties of the images of the objects described in the metadata received from each camera, the host computer system may select a preferable video feed containing images of the moving object for display to a user.

Tracking moving objects using a camera network

An example of a system described herein includes a gateway (52) configured to obtain video content and metadata relating to the video content from a plurality of network devices (20, 42), a metadata processing module (82) communicatively coupled to the gateway (52) and configured to filter the metadata according to one or more criteria to obtain a filtered set of metadata, a video processing module (80) communicatively coupled to the gateway (52) and the metadata processing module (82) and configured to isolate video portions, of the video content, associated with respective first portions of the filtered set of metadata, and a cloud services interface (86) communicatively coupled to the gateway (52), the metadata processing module (82) and the video processing module (80) and configured to provide at least some of the filtered set of metadata or the isolated video portions to a cloud computing service (90).

Cloud-based video surveillance management system

A video surveillance system includes: an input configured to receive indications of images each comprising a plurality of pixels; a memory; and a processing unit communicatively coupled to the input and the memory and configured to: analyze the indications of the images; compare the present image with a short-term background image stored in the memory; compare the present image with a long-term background image stored in the memory; provide an indication in response to an object in the present image being disposed in a first location in the present image, in a second location in, or absent from, the short-term background image, and in a third location in, or absent from, the long-term background image, where the first location is different from both the second location and the third location.

Stopped object detection

Embodiments of the disclosure provide for systems and methods for creating metadata associated with a video data The metadata can include data about objects viewed within a video scene and/or events that occur within the video scene Some embodiments allow users to search for specific objects and/or events by searching the recorded metadata In some embodiments, metadata is created by receiving a video frame and developing a background model for the video frame Foreground object(s) can then be identified in the video frame using the background model Once these objects are identified they can be classified and/or an event associated with the foreground object may be detected The event and the classification of the foreground object can then be recorded as metadata

Farzin Aghdasi

Papers

Camera calibration using feature identification

Tracking moving objects using a camera network

Cloud-based video surveillance management system

Stopped object detection

Searching recorded video