Avigilon Fortress Corporation

Motorola Solutions, Inc.

Objectvideo, Inc

Avigilon Corporation

Diamondback Vision, Inc.

Peter L Venetianer

Zhong Zhang

Alan J Lipton

Weihong Yin

Li Yu

Niels Haering

Gary W Myers

Andrew J Chosak

Pietro Russo

Paul C Brewer

W Andrew Scanlon

Yongtong Hu

Himaanshu Gupta

John I W Clark

Zeeshan Rasheed

Haiying Liu

Dana Eubanks

Matthew F Frazier

Andrew J Martone

Omar Javed

Donald Gerard Madden

Mahesh Saptharishi

Yanyan Hu

Khurram Hassan-Shafique

Kevin Piette

Mark Allmen

James S Sfekas

Dharanish Kedarisetti

Shervin Sabripour

Aleksey Lipchin

Paul D Steinberg

Bo Yang Yu

Moussa Doumbouya

Robert A Cutting

Thomas M Strat

William E Severson

Tasuki Hirata

Sven Rebien

Thomas E Slowe

Qifan Liang

Ryan Lee

Steve Titus

Wei Hong Yin

William Fincannon

Joseph Rafferty

Thomas Christopher Hansen

John B Preston

David J Staudacher

Geoffrey Egnal

Nicholas John Alcock

Michael C Mannebach

Mun Wai Lee

Xiao Xiao

Geoffrey R Taylor

James Morris

Chung-Cheng Yen

Xiao Chun Cao

Amit Mistry

Pramod Kalapa

John Ian Wallace Clark

Bruce Thompson

Kelly Schomburg

Chan Yi Siew

Burak Kakillioglu

John F Tilki

Caspar Horne

Mauricio Leal Villarreal

Keshav Thirumalai Seshadri

John Iw Clark

Mark C Allmen

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.

Imaging system with object recognition feedback

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.

Time-multiplexed illumination blending

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.

Access control system and method to distinguish between tailgate and piggyback

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.

Hybrid video analytics for small and specialized object detection

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.

Device, method and system for pruning video analytics parameters

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.

Device, method and system for installing video analytics parameters at a video analytics engine

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.

System and method for video analytics for thermography procedure compliance

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.

Object tracking and best shot detection system

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.

Alias capture to support searching for an object-of-interest

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.

Dynamic radiometric thermal imaging compensation

Techniques for automatically detecting objects left behind. An example method includes receiving video frames of a scene from a three-dimensional (3D) camera and establishing, based on 3D depths associated with the video frames, a background of the scene. The method also includes detecting, in the frames, a foreground blob in the scene based on the foreground blob having a 3D depth that is different from the background. The method further includes detecting that the foreground blob has separated into a blob corresponding to a person and a second blob corresponding to an object, based on the person having a 3D depth that is different from the object. The method additionally includes determining that the person has been separated from the object for a threshold, and responsive to determining that the person has been separated from the object for the threshold, generating an alert indicating that the object is left behind.

Object left behind detection

Disclosed is a process for performance monitoring and feedback for facial recognition systems. A first image for image matching from a camera capture device at a first location is received for purposes of image matching. A highest match confidence score of the first image to a particular stored enrollment image is determined. One or more image or user characteristics associated with the first image or first user is identified. The identified image or user characteristics and highest match confidence score are added to a facial recognition monitoring and feedback model. Subsequently, a particular one of the stored image or user characteristics consistently associated with a below-threshold highest match confidence score is identified, and a notification is displayed or transmitted including an indication of an identified facial recognition low match pattern and identifying the particular one of the stored image characteristics or user characteristics.

Device, system, and method for performance monitoring and feedback for facial recognition systems

A device, method and system for adjusting a configuration of a camera device is provided. An example device is in communication with an example camera device having a configuration. The device receives an image sequence from the camera device. The device processes the image sequence to determine a predicted location of a target object identified in the image sequence based on movement of the target object in the image sequence. The device adjusts the configuration of the camera device based on a stored configuration of the camera device, the stored configuration indicative of conditions at the predicted location.

Device, method and system for adjusting a configuration of a camera device

A method and system for processing images for a search is provided, including: receiving a plurality of images selected from search results; for each image in the plurality of images, retrieving a feature vector associated with the image; selecting a subset of the feature vectors based on similarity of feature vectors associated with the images in the plurality of images; and performing a search for feature vectors in a database similar to the feature vectors in the subset of feature vectors.

System and method for improving speed of similarity based searches

One example electronic computing device includes electronic processor configured to store a list of a plurality of video cameras being considered for an upgrade. The electronic processor may be further configured to receive first video footage captured by a first video camera and second video footage captured by a second video camera. The electronic processor may be further configured to analyze the first video footage and the second video footage to generate a plurality of upgrade priority scores. In response to determining that a first upgrade priority score of the first video camera is greater than a second upgrade priority score of the second video camera, the electronic processor may be further configured to output an upgrade priority notification configured to inform a user that the first video camera has a higher upgrade priority than the second video camera.

Analytics for planning an upgrade to a video camera surveillance system

Growing community of inventors

McLean, VA, United States of America

Peter L Venetianer