Motorola Solutions, Inc.

Avigilon Corporation

Avigilon Analytics Corporation

Videoiq, Inc.

Aleksey Lipchin

Mahesh Saptharishi

Igor Reyzin

Dimitri A Lisin

Xiao Xiao

Sergey Veselkov

Chia Ying Lee

Lu He

Maksim Bolonkin

Peter L Venetianer

Shaun P Marlatt

Yanyan Hu

Yin Wang

Moussa Doumbouya

Nicholas John Alcock

Brenna Randlett

Tulio De Souza Alcantara

Alexander Chau

Levi Glozman

Mitul Saha

Sreemanananth Sadanand

Richard Butt

Keshav T Seshadri

Pietro Russo

David J Staudacher

Kaveh Malakuti

Ron Wilson

Aral Sarrafi

Kangyan Liu

Ying Wang

Mauricio Leal Villarreal

Burak Kakillioglu

Brandon Miles

Yang Liu

Examples provide a video security system including an electronic processor configured to detect an anomaly by obtaining video data captured using an image sensor and determining a number of people detected in each of a plurality of frames of the video data. For each moment t, the electronic processor determines an average number of people n over a time interval (t-Δt, t), where Δtis greater than or equal to the moment t. For each non-overlapping time interval Δt, the electronic processor updates a histogram based on the average number of people for the time interval Δt. In response to determining, using the histogram, that the probability ρ of the average number of people n over the time interval (t-Δt, t) is less than a threshold probability ρ*, the electronic processor generates an alert indicating that an anomalous crowd size is detected.

Crowd anomaly detection

Examples provide an imaging system including an electronic processor configured to obtain video data of a scene including a region of interest including a plurality of frames, determine a total number of detected humans in the region of interest of a current frame of the plurality of frames, determine a number of running humans in the region of interest of the current frame, and determine a probability associated with the number of running humans in the region of interest of the current frame and the total number of detected humans in the region of interest of the current frame based on accumulated statistics for the region of interest. In response to determining that the probability is less than a threshold probability, the electronic processor generates an alert indicating a stampede is detected in the region of interest.

Stampede detection

A device, method and system for identifying objects in warped images from a fisheye camera is provided. A device converts warped images from a fisheye camera into dewarped images. The device determines that a first object identified in the dewarped images is: entering a blind-spot region of the dewarped images; or located at a first edge region of the dewarped images. The device determines that a second object is: exiting the blind-spot region of the dewarped images, or located at a second edge region of the dewarped images, the second edge region opposite the first edge region. The device, in response to the second object meeting one or more reidentification conditions associated with the first object, reidentifies the second object as the first object at the dewarped images.

Device, method and system for identifying objects in warped images from a fisheye camera

Systems and method for camera calibration. In one example, the camera includes an electronic processor configured to obtain a bounding box in a captured image, the bounding box bounding a person in the image and having a lowermost vertical coordinate and an uppermost vertical coordinate. The electronic processor determines an initial estimation of values of a set of camera parameters Θ and a current estimation of values of the set of camera parameters Θ based on the initial estimation. The electronic processor iteratively updates the current estimation of values of the set of camera parameters Θ by determining feet coordinates within the bounding box, determining a vertical head coordinate, and updating the current estimation of values of the set of camera parameters Θ by reducing a difference between the vertical head coordinate and the uppermost vertical coordinate of the bounding box.

Automatic camera calibration

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

Examples provide a video security system including an electronic processor configured to detect an anomaly by obtaining video data captured using an image sensor and determining a number of people detected in each of a plurality of frames of the video data. For each moment t, the electronic processor determines an average number of people n over a time interval (t−Δt1, t), where Δt1 is greater than or equal to the moment t. For each non-overlapping time interval Δt1, the electronic processor updates a histogram based on the average number of people for the time interval Δt1. In response to determining, using the histogram, that the probability ρ of the average number of people n over the time interval (t−Δt1, t) is less than a threshold probability ρ*, the electronic processor generates an alert indicating that an anomalous crowd size is detected.

A computer-implemented method of localizing an image of a person captured using a camera is provided, the person in the field of view of a camera, comprising: obtaining the image captured using a camera, the image comprising the person within a bounding box; determining at least one slant value associated with the person within the bounding box; determining head image coordinates and feet image coordinates for the person using the at least one slant value; and localizing the person by projecting the head image coordinates to a head plane and the feet image coordinates for the person to a ground plane.

Method and system for calibrating a camera and localizing objects within the camera field of view

A method of detecting unusual motion is provided, including: determining features occurring during a fixed time period; grouping the features into first and second subsets of the fixed time period; grouping the features in each of the first and second subsets into at least one pattern interval; and determining when an unusual event has occurred using at least one of the pattern intervals.

Unusual motion detection method and system

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

Methods, systems, and techniques for automatic camera calibration. One or more calibration images are captured using a camera. The calibration images depict one or more bounding boxes, and each of the bounding boxes bounds a person. For each of the bounding boxes, the person is modeled using a rectangle or a parallelepiped, and a projection of the rectangle or parallelepiped is determined. A mapping that maps foot vertices of the projection to head vertices of the projection is determined, and using the foot vertices and the mapping, estimates of the head vertices and distances between the head vertices and the estimates of the head vertices are determined. The camera is calibrated by iteratively updating, using an objective function, the camera parameters so as to reduce those distances.

Method for calibrating a camera using bounding boxes, and camera configured to perform that method

One embodiment provides an access control system including access control sensors to detect actions performed in a vicinity of an access point, a verification sensor to verify access of the access point, and an electronic processor communicatively coupled to the access control sensors and the verification sensor. The electronic processor is configured to in response to an access intent model satisfying an accuracy condition, deploy the access intent model for the access point and receive a dataset indicating an action performed in the vicinity of the access point. The electronic processor is also configured to predict an access intent to access the access point by applying the access intent model to the dataset and enable access through the access point. The electronic processor is further configured to receive verification data indicating whether the access point is accessed, and automatically assign a label to the dataset based on the verification data.

Autonomous self-learning artificial intelligence intent system for access control

There is provided an appearance search system comprising one or more cameras configured to capture video of a scene, the video having images of objects. The system comprises one or more processors and memory comprising computer program code stored on the memory and configured when executed by the one or more processors to cause the one or more processors to perform a method. The method comprises identifying one or more of the objects within the images of the objects. The method further comprises implementing a learning machine configured to generate signatures of the identified objects and generate a signature of an object of interest. The system further comprises a network configured to send the images of the objects from the camera to the one or more processors. The method further comprises comparing the signatures of the identified objects with the signature of the object of interest to generate similarity scores for the identified objects, and transmitting an instruction for presenting on a display one or more of the images of the objects based on the similarity scores.

System and method for appearance search

A method, system and computer program product for self-learned and probabilistic-based prediction of inter-camera object movement is disclosed. The method includes building and storing a transition model defined by transition probability and transition time distribution data generated during operation of a first video camera and one or more other video cameras over time. The method also includes employing at least one balance flow algorithm on the transition probability and transition time distribution data to determine a subset of the video cameras to initiate a search for an object based on a query. The method also includes running the search for the object over the subset of the video cameras.

Growing community of inventors

Newton, MA, United States of America

Aleksey Lipchin