Object Video Labs, LLC

Alarm.com Incorporated

Avigilon Fortress Corporation

Allison Beach

Donald Gerard Madden

Gang Qian

Narayanan Ramanathan

Sima Taheri

Sung Chun Lee

Sravanthi Bondugula

Daniel Todd Kerzner

Weihong Yin

Benjamin Asher Berg

David James Hutz

Christopher Silverman

Dana Eubanks

Andrew Scanlon

Zhong Zhang

Jeffrey A Bedell

Stephen Scott Trundle

Ethan Shayne

David Conger

Yunxian Zhou

Eduardo Romera Carmena

Matthew Daniel Correnti

Abraham Joseph Kinney

Robert Nathan Picardi

Zeeshan Rasheed

Michael Kelly

Jangwon Lee

Dave Conger

Kyoung-Jin Park

Michael Li

Kyle Glowacki

Babak Rezvani

Alexander Lawrence Reeder

Glenn Tournier

Don Madden

Donald Faraci

Huiping Li

Adam Gordon

John Stanley

James Siemer

Jennifer Doughty

Adam Rodriguez

Aaron Lee Roberts

Bret Jutras

Achyut Boggaram

Robert S Hastings

Stephanie Hinderks

Nafiseh Salmani Niyasar

Yuanwei Wu

Sravanti Bondugula

James Jay Glickstein

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for object detection. One of the methods includes determining, using first sensor data, a detection result on whether to trigger an event alerting a presence of an object in a target area by executing one or more models; determining, using second sensor data, a ground truth for the event that indicates whether an object is present in the target area; determining a difference value by comparing the detection result and the ground truth; adjusting at least one parameter of the one or more models in response to determining that the difference value does not satisfy the one or more threshold criteria; and determining a new detection result on whether to trigger a second event by executing the one or more models with adjusted parameters using new first sensor data.

Using implicit event ground truth for video cameras

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for spatial motion attention for intelligent video analytics. One of the methods includes: obtaining an input image of a region; generating a motion image that characterizes a difference between a value of a pixel at the pixel location in the input image and a value of a pixel at the pixel location in the reference image; generating a feature map using the input image; generating, using the motion image and the feature map, a motion enhanced feature map that has, for one or more pixels that likely indicate movement, a first value that a) indicates that the corresponding pixel in the motion enhanced feature map likely indicates movement and b) is different from a second value for a corresponding pixel in the feature map; and analyzing the motion enhanced feature map.

Spatial motion attention for intelligent video analytics

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving multiple images from a camera, each image of the multiple images representative of a detection of an object within the image. For each image of the multiple images the methods include: determining a set of detected objects within the image, each object defined by a respective bounding box, and determining, from the set of detected objects within the image and ground truth labels, a false detection of a first object. The methods further include determining that a target object threshold is met based on a number of false detections of the first object in the multiple images, generating, based on the number of false detections for the first object meeting the target object threshold, an adversarial mask for the first object, and providing, to the camera, the adversarial mask.

Adversarial masks for scene-customized false detection removal

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for visual authentication. In some implementations, a method may include obtaining images of a person at a property; detecting a discontinuity in an appearance of the person in the images of the person at the property; determining that the discontinuity does not correspond to a known occlusion; and providing an indication of a potential spoofing attack.

Spoofing attack detection

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for objection classification. One of the methods includes: obtaining a first set of images of objects that have a likelihood of being at a property that satisfies a likelihood threshold; generating, for each object, a binary classifier from a set of images of the respective object; determining, using at least one of the binary classifiers, that an image of an unknown object was classified as an object from the objects; in response to determining, using the binary classifiers, that the image of the unknown object was classified as an object from the objects, selecting a second set of images of unknown objects that does not include the image; and generating a multiclass classifier for use classifying objects using i) the first set as respective classes and ii) the second set that does not include the image.

Training an object classifier with a known object in images of unknown objects

Methods, systems, and apparatus, including computer programs encoded on a storage device, for monitoring a vehicle are disclosed. A vehicle monitoring system for monitoring a vehicle includes one or more processors and one or more computer storage media storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations. The operations include: receiving, from a vehicle occupancy monitor and by the vehicle monitoring system, vehicle occupancy data that indicates presence of a vehicle occupant; receiving, by the vehicle monitoring system and from a sensor, sensor data that indicates an attribute of the vehicle; analyzing, by the vehicle monitoring system, the vehicle occupancy data and the sensor data; and based on analyzing the vehicle occupancy data and the sensor data, performing a vehicle monitoring system action.

Vehicle occupancy monitor

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling access to a premises. One of the methods includes detecting a person within a threshold distance of a door; determining that there is a threshold likelihood that the person may access the door; providing instructions that indicate one or more criteria for access to the door; determining that a threshold quantity of the one or more criteria are satisfied; and in response to determining that the threshold quantity of the one or more criteria are satisfied, providing access to the door.

Access control system

Disclosed are methods, systems, and apparatus for monitoring delivered packages using video. A method includes obtaining a notification of a delivery of a package at a property that includes a first image depicting the package; obtaining a second image captured by a camera at the property; determining, using the first image and the second image, that the package has been delivered to the property; generating a package model that represents an appearance of the package; and tracking a location of the package for use in determining whether to provide an alert to a device about the location of the package. Determining that the package has been delivered to the property comprises: comparing the first image to a model of a scene of the property; determining that the first image satisfies similarity criteria for matching the model of the scene; and determining that the second image likely depicts the package.

Monitoring delivered packages using video

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting packages delivering in the camera's blindspot. One of the methods includes detecting, using one or more images captured by a camera at a property, movement in an area of interest i) at the property, ii) that is included in a field of view of the camera and iii) was generated using historical data for packages delivered to the property; determining, using the detected movement in the area of interest, that a package was likely delivered; and in response to determining that the package was likely delivered, performing one or more automated actions for the package.

Camera blindspot analysis

Methods and systems, including computer-readable media, are described for monitoring presence or absence of an object at a property using local region matching. A system generates images while monitoring an area of the property and, based on the images, detects an object in a region of interest in the area. For each of the images: the system iteratively computes interest points for the object using photometric augmentation applied to the image before each iteration of computing the interest points. A digital representation of the region and the object is generated based on interest points that repeat across the images after each application of the photometric augmentation. Based on the digital representation, a set of anchor points are determined from the interest points that repeat across images. Using the set of anchor points, the system detects an absence or a continued presence of the object in the area of the property.

Monitoring presence or absence of an object using local region matching

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using dual descriptor data. One of the methods includes: detecting, using a first set of descriptor features included in dual descriptor data, a first representation within first image data collected by a camera; determining a change to an imaging modality of the camera; detecting, using a second set of features included in the dual descriptor data, a second representation within second image data collected by the camera; classifying the first representation and the second representation as associated with a same object using the dual descriptor data; and in response to classifying the first representation and the second representation as associated with the same object using the dual descriptor data, transmitting operational instructions to one or more appliances connected to the system.

Dual descriptor data for object recognition in low light conditions

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for monitoring events using a Virtual Inductive Loop system. In some implementations, image data is obtained from cameras. A region depicted in the obtained image data is identified, the region comprising lines spaced by a distance that satisfies a distance threshold. For each line included in the region: an object depicted crossing the line is determined whether to satisfy a height criteria indicating that the line is activated. In response to determining that an object depicted crossing the line satisfies the height criteria, an event is determined to have likely occurred using data indicating (i) which lines of the lines were activated and (ii) an order in which each of the lines were activated. In response to determining that an event likely occurred, actions are performed using at least some of the data.

Vehicular access control based on virtual inductive loop

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reducing camera false detections. One of the methods includes providing, to a neural network of an image classifier that is trained to detect objects of two or more classification types, a feature vector for a respective training image; receiving, from the neural network, an output vector that indicates, for each of the two or more classification types, a likelihood that the respective training image depicts an object of the corresponding classification type; accessing, from two or more ground truth vectors each for one of the two or more classification types, a ground truth vector for the classification type of an object depicted in the training image; and adjusting one or more weights in the neural network using the output vector and the ground truth vector; and storing, in a memory, the image classifier.

Reducing false detections for night vision cameras

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for retroactive event detection. The methods, systems, and apparatus include actions of obtaining an image captured by a camera at a current time, determining that the image depicts a change in a region from a previous image captured by the camera at a previous time, determining, based on determining that the image depicts the change in the region, whether the change depicted in the image is of a known object type, determining, based on the determination that the change depicted in the image is of a known object type, whether the change does not correspond to a previously detected event, and determining, based on the determination that the change does not correspond to a previously detected event, whether the images captured by the camera between the current time and the previous time depict an event.

Event detection in video surveillance

Disclosed are methods, systems, and apparatus for object localization in video. A method includes obtaining a reference image of an object; generating, from the reference image, homographic adapted images showing the object at various locations with various orientations; determining interest points from the homographic adapted images; determining locations of an object center in the homographic adapted images relative to the interest points; obtaining a sample image of the object; identifying matched pairs of interest points, each matched pair including an interest point from the homographic adapted images and a matching interest point in the sample image; and determining a location of the object in the sample image based on the locations of the object center in the homographic adapted images relative to the matched pairs. The method includes generating a homography matrix; and projecting the reference image of the object to the sample image using the homography matrix.

Object localization in video

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Leesburg, VA, United States of America

Allison Beach