3vr Security, Inc.

Stephen D Fleischer

Robert Vallone

Gordon T Haupt

Stephen G Russell

J Andrew Freeman

Timothy B Frederick

Colvin Pitts

Shannon Hale

Michael E Wells

Christian Pappas

Thomas W Kirkman

Robert P Vallone

A method and apparatus is described for specifying regions of interest within a two-dimensional view of visual information that comprises a series of frames. Visual changes that occur in the view are stored. A user enters search criteria that specify at least one first region of interest within the view and a visual change. A visual change may include a change in pixel values or a detection of motion of one or more objects within the view. The first search criteria are compared against the stored visual changes to identify a sequence of frames in which the specified visual change occurred within the first region of interest. The search criteria may specify multiple regions of interest, each with one or more types of visual changes. If a motion is specified, then a direction, speed, and behavior of a moving object may also be specified.

Specifying search criteria for searching video data

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 for searching for probable matches in a video surveillance system is disclosed. A new event, such as a face captured in an image set, is matched against other events in a database of events. A similarity score is generated based on the difference between the new event and other events in the database. The similarity score may be weighted by information external to the image sets. Because of limited system resources, an association between a new event and every other event in the system may not be kept. Thus, when searching for probable matches of a particular event, some events that are related to the particular event may not be initially selected. Such events may be associated with an event in a first set of events that are associated with the particular event. Therefore, a second set of events is selected that are associated with the first set of events.

Automated searching for probable matches in a video surveillance system

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 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.

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.

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San Francisco, CA, United States of America

Stephen D Fleischer