3vr Security, Inc.

J Andrew Freeman

Robert Vallone

Stephen G Russell

Gordon T Haupt

Stephen D Fleischer

Timothy B Frederick

Christian Pappas

Thomas W Kirkman

Marcus S Marinelli

Robert P Vallone

A method of identifying an object captured in a video image in a multi-camera video surveillance system is disclosed. Sets of identifying information are stored in profiles, each profile being associated with one object. The disclosed method of identifying an object includes comparing identifying information extracted from images captured by the video surveillance system to one or more stored profiles. A confidence score is calculated for each comparison and used to determine a best match between the extracted set of identifying information and an object. In one embodiment, the method is used as part of a facial recognition system incorporated into a video surveillance system.

Interactive system for recognition analysis of multiple streams of video

A technique is disclosed for determining when to close a group of a plurality of groups. A closed group is one to which an image set may not be added. Each group includes one or more image sets. Each image set includes one or more images of at least one object. Each group corresponds to an object that is common among images in the one or more image sets that belong to the group. Determining when to close a particular group is based, at least in part, on one or more factors, such as how many image sets are in the particular group, the length of time the particular group has been open, and data about the one or more image sets in the particular group.

Grouping items in video stream images into events

A method that determines whether a detected event is a significant event requiring action in a video surveillance system. Determined event attributes and object attributes are analyzed to determine if the potential event should trigger an action by the surveillance system. If the potential event should trigger an action, at least one action is triggered. Actions may include relevant event attribute and object attribute information. Potential event may include events detected by a sensor, or external events communicated by an external system to the surveillance system. Event attributes may include location and type of the potential event. Object attributes may include an identification of an object, and attributes associated with the identified object. If an object cannot be positively identified, object attributes may include potential object identifications or determined group associations of the object.

Intelligent event determination and notification in a surveillance system

A method of managing video data storage in a video surveillance system is disclosed. The disclosed methods extend the amount of calendar time for which video and image data can be stored on a storage device. The disclosed methods apply decision criteria, such as rules, configuration data and preferences, to support intelligent automatic reduction of stored surveillance data such that images and video data of most interest are maintained while less important data is deleted, compressed or archived.

Method for automatically reducing stored data in a surveillance system

A pipeline architecture for analyzing multiple streams of video is embodied, in part, in a layer of application program interfaces (APIs) to each stage of processing. Buffer queuing is used between some stages, which helps moderate the load on the CPU(s). Through the layer of APIs, innumerable video analysis applications can access and analyze video data flowing through the pipeline, and can annotate portions of the video data (e.g., frames and groups of frames), based on the analyses performed, with information that describes the frame or group. These annotated frames and groups flow through the pipeline to subsequent stages of processing, at which increasingly complex analyses can be performed. At each stage, portions of the video data that are of little or no interest are removed from the video data. Ultimately, 'events' are constructed and stored in a database, from which cross-event and historical analyses may be performed and associations with, and among, events may be made.

Pipeline architecture for analyzing multiple video streams

Deep frame analysis of multiple video streams in a pipeline architecture

Event generation and camera cluster analysis of multiple video streams in a pipeline architecture

A pipeline architecture for analyzing multiple streams of video is embodied, in part, in a layer of application program interfaces (APIs) to each stage of processing. Buffer queuing is used between some stages, which helps moderate the load on the CPU(s). Through the layer of APIs, innumerable video analysis applications can access and analyze video data flowing through the pipeline, and can annotate portions of the video data (e.g., frames and groups of frames), based on the analysis performed, with information that describes the frame or group. These annotated frames and groups flow through the pipeline to subsequent stages of processing, at which increasingly complex analysis can be performed. At each stage, portions of the video data that are of little or no interest are removed from the video data. Ultimately, 'events' are constructed and stored in a database, from which cross-event and historical analysis may be performed and associations with, and among, events may be made.

Correlation processing among multiple analyzers of video streams at stages of a pipeline architecture

Feed-customized processing of multiple video streams in a pipeline architecture

A technique is disclosed for determining a group in which to add a new image set. The new image set is captured by one or more cameras in a video surveillance system. Similarity scores are generated between the new image set and one or more image sets of a plurality of groups, wherein each group includes one or more image sets of at least one object. The new image set is added to a group based on one or more factors. Also, a technique is disclosed for determining when to close a group, wherein a closed group is one to which an image set may not be added.

Growing community of inventors

San Jose, CA, United States of America

J Andrew Freeman