Abstract: Examples provide a system for detecting large vehicles. The system includes an electronic processor configured to obtain video data from an image sensor, determine a bounding box bounding a detected vehicle in a plurality of frames of the video data, determine a trajectory of the detected vehicle based on changes in location of the bounding box in the plurality of frames, and determine a position of the detected vehicle within at least one frame of the plurality of frames. Based on the trajectory of the detected vehicle, the bounding box, and the position of the detected vehicle within the at least one frame, the electronic processor determines a length of the detected vehicle, and determines whether the length is greater than a threshold. In response to the length of the detected vehicle being greater than a threshold, the electronic processor makes a determination that a large vehicle is detected.

Abstract: A camera system includes an electronic processor configured to generate a three-dimensional scene including static assets and dynamic assets. The static assets and the dynamic assets are representative of an environment and objects within a reference video. The reference video represents a first perspective. The electronic processor is configured to animate the dynamic assets based on motions of the objects within the reference video to generate an animated scene, render a test video based on the animated scene. The test video represents a second perspective. The electronic processor is configured to generate video analytics data of the test video, generate a comparison based on the video analytics data of the test video and video analytics data of the reference video, and transform a graphical user interface according to the comparison.

Abstract: Systems and methods for controlling camera settings of a camera to improve detection of faces in an uncontrolled environment are described. A first image is received from the camera, where the first image is captured by the camera at a first set of camera settings. A face is detected in the first image. The camera is adjusted to a second set of camera settings based on the detected face, where the second set of camera settings different from the first set of camera settings. A second image is received from the camera, where the second image is captured by the camera at the second set of camera settings. The face is detected in the second image. A quality metric of the face in the second image is determined where the quality metric is indicative of an image quality of the face in the second image.

Abstract: A system and method for improving a video characteristic of a video stream is described. According to various implementations of the invention, a changed region between a later-in-time image frame and an earlier-in-time image frame and an unchanged region between such two image frames are determined. A new improvement to the video characteristic is determined and applied to the changed region of the later-in-time image frame. A prior improvement to the video characteristic that was determined for the earlier-in-time image frame is applied to the unchanged region of the later-in-time image frame.

Abstract: Examples provide a variable zoom imaging system including an image sensor, a set of illumination channels, and a current driver circuitry for driving the set of illumination channels. Each respective illumination channel includes a set of light sources and a lens, and is associated with a respective focus distance. An electronic processor is configured to focus the image sensor to an object of interest, determine a distance from the image sensor to the object of interest, determine an exposure period for the image sensor, select a subset of illumination channels to activate based at least on the distance from the image sensor to the object of interest, and capture a first image of the object of interest using the image sensor by sequentially activating and deactivating each selected illumination channel of the subset for first respective time frames within the exposure period.

Abstract: Access control system and method to distinguish between tailgate and piggyback. The method includes receiving, from a camera positioned at an access point, one or more images of a first person at the access point and determining based on credential information whether the first person is authorized to enter through the access point. In response to determining that the first person is authorized to enter through the access point, the method includes determining, based on a consent-to-follow gesture of the first person detected in the one or more images, a number of guests of the first person requested to enter through the access point. The method also includes determining, based on the one or more images, a number of persons entering through the access point and generating an alert indicating unauthorized entry when the number of persons is greater than the number of guests.

Abstract: One aspect provides a method for object detection including detecting, using an electronic processor, a plurality of candidate objects in a video using a convolutional neural network detection process and a background subtraction detection process and identifying, using the electronic processor, a candidate object from the plurality of candidate objects. The candidate object detected by the background subtraction detection process in a location of the video with no candidate objects detected by the convolutional neural network detection process. The method also includes determining, using the electronic processor, a background subtraction confidence level of the candidate object and categorizing, using the electronic processor, the candidate object as a detected object in the video in response to the background subtraction confidence level satisfying a background subtraction confidence threshold.

Abstract: Examples provide an imaging system including an image sensor; and an electronic processor in communication with the image sensor. The electronic processor is configured to determine a set of imaging parameters, and, for each iteration included in an iteration set, update the set of imaging parameters by: receiving an image captured by the image sensor using the set of imaging parameters, performing object recognition on the image to detect an object of interest, determining an object recognition confidence score, determining a set of external factors based on the image and/or information from a second sensor, linking the set of imaging parameters and the set of external factors to the object recognition confidence score, and training a predictive model for updating the set of imaging parameters using the object recognition confidence score, the set of imaging parameters, and the set of external factors.

Abstract: A device, method and system for pruning video analytics parameters is provided. A device analyzes images from a camera installed at an installation location using a full initial machine learning model trained to detect objects in the images using video analytics parameters, the full initial machine learning model being independent of the installation location. The device gathers, for a period of time, statistics for: frequency of use of the video analytics parameters and/or error generation frequency in detecting the objects in the images. After the period of time, based on the statistics, the device determines that given video analytics parameters meets one or more pruning conditions; and, in response, prunes the given video analytics parameters such that the full initial machine learning model, initially provided for the camera, changes to an updated machine learning model for the camera.

Abstract: Systems and methods for controlling camera settings of a camera to improve detection of faces in an uncontrolled environment are described. A first image is received from the camera, where the first image is captured by the camera at a first set of camera settings. A face is detected in the first image. The camera is adjusted to a second set of camera settings based on the detected face, where the second set of camera settings different from the first set of camera settings. A second image is received from the camera, where the second image is captured by the camera at the second set of camera settings. The face is detected in the second image. A quality metric of the face in the second image is determined where the quality metric is indicative of an image quality of the face in the second image.

Abstract: A system and method for improving a video characteristic of a video stream is described. According to various implementations of the invention, a changed region between a later-in-time image frame and an earlier-in-time image frame and an unchanged region between such two image frames are determined. A new improvement to the video characteristic is determined and applied to the changed region of the later-in-time image frame. A prior improvement to the video characteristic that was determined for the earlier-in-time image frame is applied to the unchanged region of the later-in-time image frame.

Abstract: One aspect provides a method for object detection including detecting, using an electronic processor, a plurality of candidate objects in a video using a convolutional neural network detection process and a background subtraction detection process and identifying, using the electronic processor, a candidate object from the plurality of candidate objects. The candidate object detected by the background subtraction detection process in a location of the video with no candidate objects detected by the convolutional neural network detection process. The method also includes determining, using the electronic processor, a background subtraction confidence level of the candidate object and categorizing, using the electronic processor, the candidate object as a detected object in the video in response to the background subtraction confidence level satisfying a background subtraction confidence threshold.

Abstract: A device, method and system for installing video analytics parameters at a video analytics engine is provided. An example device determines that a classification of a first camera is one or more of similar to, and same as, a respective classification of at least one second camera. The example device retrieves, from a memory, video analytics parameters associated with the at least one second camera, the video analytics parameters stored at the memory in association with the respective classification. The example device causes installing of the video analytics parameters at a video analytics engine associated with the first camera.

Abstract: Disclosed is a process for implementing an automated analytics for insuring compliance for a thermographic protocol for subjects seeking a temperature check, perhaps for entrance or access to a controlled space or facility. The automated video analytics utilize one or more cameras to detect thermography compliance violations based on whether the subject is perspiring, dehydrated, recently consumed a beverage, has exposed skin, excessive clothing, the amount and type of activity before getting screened, and/or the external and internal temperatures at the controlled facility associated with the subject. Furthermore, the automated video analytics may create a non-compliance score and/or control a timer for a non-compliance detection. Also, short and long term collected data may be analyzed for compliance to guidelines.

Abstract: A system and method for improving a video characteristic of a video stream is described. According to various implementations of the invention, a changed region between a later-in-time image frame and an earlier-in-time image frame and an unchanged region between such two image frames are determined. A new improvement to the video characteristic is determined and applied to the changed region of the later-in-time image frame. A prior improvement to the video characteristic that was determined for the earlier-in-time image frame is applied to the unchanged region of the later-in-time image frame.

Abstract: A method and system using face tracking and object tracking is disclosed. The method and system use face tracking, location, and/or recognition to enhance object tracking, and use object tracking and/or location to enhance face tracking.

Abstract: Access control system and method to distinguish between tailgate and piggyback. The method includes receiving, from a camera positioned at an access point, one or more images of a first person at the access point and determining based on credential information whether the first person is authorized to enter through the access point. In response to determining that the first person is authorized to enter through the access point, the method includes determining, based on a consent-to-follow gesture of the first person detected in the one or more images, a number of guests of the first person requested to enter through the access point. The method also includes determining, based on the one or more images, a number of persons entering through the access point and generating an alert indicating unauthorized entry when the number of persons is greater than the number of guests.

Abstract: Systems and methods for dynamic radiometric thermal imaging compensation. The method includes analyzing a visible light image to determine an emissivity value for each of a plurality of visible light pixels making up the visible light image. The method includes associating each of the plurality of thermal pixels making up a thermal image corresponding to the visible light image with at least one of the plurality of visible light pixels making up the visible light image. The method includes generating a second thermal image by, for each of the plurality of thermal pixels making up the thermal image, determining a temperature value based on the thermal pixel value of the thermal pixel and the emissivity value of the at least one of the plurality of visible light pixels associated with the thermal pixel.

Abstract: Alias capture to support searching for an object-of-interest is disclosed. A method includes capturing, using a camera with a defined field of view, video image frames that include a moving object-of-interest. The method also includes tracking the object-of-interest over a period of time starting when the object-of-interest enters the field of view and ending when the object-of-interest exits the field of view. The method also includes detecting, at a point in time in-between the start and end of the period of time of the tracking, a threshold exceeding change in an appearance of the object-of-interest. The method also includes creating, before the end of the period of time of the tracking, a new object profile for the object-of-interest in response to the detecting of the threshold exceeding change.

Abstract: Systems and methods for dynamic radiometric thermal imaging compensation. The method includes analyzing a visible light image to determine an emissivity value for each of a plurality of visible light pixels making up the visible light image. The method includes associating each of the plurality of thermal pixels making up a thermal image corresponding to the visible light image with at least one of the plurality of visible light pixels making up the visible light image. The method includes generating a second thermal image by, for each of the plurality of thermal pixels making up the thermal image, determining a temperature value based on the thermal pixel value of the thermal pixel and the emissivity value of the at least one of the plurality of visible light pixels associated with the thermal pixel.