Object Video Labs, LLC

Alarm.com Incorporated

Avigilon Fortress Corporation

Narayanan Ramanathan

Allison Beach

Donald Gerard Madden

Gang Qian

Sima Taheri

Sung Chun Lee

Sravanthi Bondugula

Kyoung-Jin Park

David James Hutz

Zhong Zhang

Weihong Yin

Benjamin Asher Berg

Ethan Shayne

Christopher Silverman

Dana Eubanks

Don Madden

Daniel Todd Kerzner

Tae Eun Choe

Eduardo Romera Carmena

Achyut Boggaram

Atul Kanaujia

Robert S Hastings

Sravanti Bondugula

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 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, 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 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

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an event detector. The methods, systems, and apparatus include actions of identifying a portion of a first interframe difference image that represents motion of an OI, determining that a second interframe difference image represents motion by a non-OI, combining the portion of the first interframe difference image and the second interframe difference image as a third interframe difference image labeled as motion of both an OI and a non-OI, and training an event detector with the third interframe difference image.

Augmenting training samples for motion detection systems using image additive operations

Disclosed are methods, systems, and apparatus for adjusting areas of interest for motion detection in camera scenes. A method includes obtaining a map of false motion event detections using a first area of interest; identifying an overlap area between the map of false detections and the first area of interest; determining a second area of interest that includes portions of the first area of interest and excludes at least a part of the overlap area; obtaining a map of true motion event detections using the first area of interest; determining whether true detections using the second area of interest compared to true detections using the first area of interest satisfies performance criteria; and in response to determining that true detections using the second area of interest compared to true detections using the first area of interest satisfies performance criteria, providing the second area of interest for use in detecting events.

Adjusting areas of interest for motion detection in camera scenes

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using feature descriptors to track objects depicted in images. One of the methods includes receiving hue, saturation, value data for an image and data that indicates an object detected in the image, generating a feature descriptor that includes hue data and saturation data, determining, for each of two or more tracked objects that each have a historical feature descriptor that includes historical hue data and historical saturation data, a distance between (i) the respective historical feature descriptor and (ii) the feature descriptor, associating the feature descriptor for the object with a tracked object from the two or more tracked objects, and tracking the tracked object in one or more images from a video sequence using the feature descriptor and the historical feature descriptor.

Object tracking with feature descriptors

Methods and systems, including computer programs encoded on a storage medium, are described for implementing item monitoring using a doorbell camera. A system generates an input video stream that has image frames corresponding to detection of activity at a property. Timing information is generated for the video stream and includes a timestamp for each image frame of the stream. Using the timing information, the system processes a pre-event image frame that precedes detection of the activity and a post-event image frame that coincides with detection of the activity. An image score is computed with respect to placement of a candidate item at the property in response to processing the pre-event and post-event image frames. The image score is used to determine that a first item was delivered to the property or that a second item was removed after being delivered to the property.

Item monitoring for doorbell cameras

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for tracking objects of interest using distance-based thresholding. One of the methods includes detecting an object depicted in an image captured by a camera, determining a predicted physical distance between the object and the camera, selecting, from a plurality of predetermined confidence thresholds, a confidence threshold for the predicted physical distance, each confidence threshold in the plurality of predetermined confidence thresholds for a different physical distance range, the confidence threshold having a physical distance range that includes the predicted physical distance, and determining, using the confidence threshold and a confidence score that indicates a likelihood that the object is an object of interest, that the object is likely an object of interest.

Object of interest detector distance-based multi-thresholding

Methods, systems, and apparatus for removing precipitation from video are disclosed. A method includes generating, from a first set of images of a scene from a camera, a segmented background image model of the scene; obtaining a second set of images from the camera; identifying, in an image of the second set of images, a plurality of edges, determining that a first edge of the plurality of edges satisfies criteria for representing precipitation based at least in part on determining that the first edge (i) does not correspond to the background image model of the scene and (ii) extends into two or more contiguous segments of the scene; in response, classifying each of the contiguous segments as a precipitation segment; generating pixel data for each of the precipitation segments; and applying the pixel data to each precipitation segment in the image.

Precipitation removal from video

Methods, systems, and apparatus for ground plane filtering of video events are disclosed. A method includes obtaining a first set of images of a scene from a camera; determining a ground plane from the first set of images of the scene; obtaining a second set of images of the scene after the first set of images of the scene is obtained; determining that movement shown by a group of pixels in the second set of images of the scene satisfies motion criteria; determining that the ground plane corresponds with at least a portion of the group of pixels; and in response to determining that movement shown by the group of pixels in the second set of images of the scene satisfies motion criteria, and that the ground plane corresponds with at least a portion of the group of pixels, classifying the group of pixels as showing ground plane based motion.

Ground plane filtering of video events

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an event detector. The methods, systems, and apparatus include actions of obtaining frames of a video, determining whether an object of interest is detected within the frames, determining whether motion is detected within the frames, determining whether the frames correspond to motion by an object of interest, generating a training set that includes labeled inter-frame differences based on whether the frames correspond to motion by an object of interest, and training an event detector using the training set.

Event detector training

Methods, systems, and apparatus for camera detection of human activity with co-occurrence are disclosed. A method includes detecting a person in an image captured by a camera; in response to detecting the person in the image, determining optical flow in portions of a first set of images; determining that particular portions of the first set of images satisfy optical flow criteria; in response to determining that the particular portions of the first set of images satisfy optical flow criteria, classifying the particular portions of the first set of images as indicative of human activity; receiving a second set of images captured by the camera after the first set of images; and determining that the second set of images likely shows human activity based on analyzing portions of the second set of images that correspond to the particular portions of the first set of images classified as indicative of human activity.

Camera detection of human activity with co-occurrence

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing virtual surveillance windows for video surveillance. The methods, systems, and apparatus include actions of obtaining an original video, generating a downscaled video from the original video, detecting a first event at a location from the downscaled video using a first classifier, generating a windowed video from the original video based on the location, detecting a second event from the windowed video, and performing an action in response to detecting the second event.

Managing virtual surveillance windows for video surveillance

Methods, and systems including computer programs encoded on a computer storage medium, for training a detection model for surveillance devices using semi-supervised learning. In one aspect, the methods include receiving imaging data collected by a camera of a scene within a field of view of the camera. Annotated training data is generated from the imaging data and one or more detection models are trained using the annotated training data. Based on a set of performance parameters, an optimized detection model is selected of the one or more detection models, and the optimized detection model is provided to the camera.

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Chantilly, VA, United States of America

Narayanan Ramanathan