Abstract: Methods and systems for creating video from multiple sources utilize intelligence to designate the most relevant sources, facilitating their adjacent display and/or catenation of their video streams.

Abstract: A computerized method of video analysis including receiving image data for a plurality of video frames depicting a scene that includes at least one of a plurality of background features. Each video frame includes s a plurality of image regions and at least one video frame has an object within at least one of the image region. A plurality of background classifications is provided that correspond to one of the background features in the scene. At least one image region is assigned a background classification based at least in part on the location of the object relative to the image region.

Abstract: Methods and system for determining, a number of objects, without tracking each of the objects, in first and second fields-of-view. First and second video frames are received from first and second image sources. The image sources have the first and second fields-of-view, and the fields-of-view are known to overlap at least in part. The number of objects is determined based on the first and second video frames and the known overlap.

Abstract: Templates are used to define generic site layouts in terms of site-independent elements. The templates are customized by site-knowledgeable users and employed within a surveillance system.

Abstract: Rules are applied to video surveillance data to detect events. Localization of the events is achieved by decomposing events into distinct components, each of which can, in some embodiments, be defined at different locations and by different users.

Abstract: Video surveillance data is received from multiple disparate, unaffiliated locations at which various items are offered for sale. Based on the content of the video surveillance data, consumer preference behavior is characterized with respect to at least some of the items. The data is consolidated and can be made available for analysis.

Abstract: A computerized method of image analysis that includes receiving first image data for a plurality of first video frames representing a first scene. Each first video frame is composed of a plurality of image regions, and a first object is present in an image region of at least one first video frame. Second image data is received for a plurality of second video frames representing a second scene. Each second video frame is composed of a plurality of image regions, and a second object is present in an image region of at least one second video frame. The method also includes determining a relationship between a first image region and a second image region based on a probabilistic correlation between occurrences of the first object being present in the first image region and occurrences of the second object being present in the second image region.

Abstract: Systems and methods for determining when a change has occurred in an area-of-interest included in an image region are disclosed. The system and methods may perform a specially modified background subtraction method. The systems and methods may initialize a background image region and then comparing a first image region from a second frame to the background image region to classify pixels as foreground pixels or background pixels. The foreground pixels are then classified based on predetermined characteristics. The background image region may then be updated to include foreground pixels that did not have the predetermined characteristics. The systems and methods allow for searching for background updates to thereby determine when a change has occurred in the area-of-interest.

Abstract: A computerized method of video analysis including receiving image data for a plurality of video frames depicting a scene that includes at least one of a plurality of background features. Each video frame includes a plurality of image regions and at least one video frame has an object within at least one of the image region. A plurality of background classifications is provided that correspond to one of the background features in the scene. At least one image region is assigned a background classification based at least in part on the location of the object relative to the image region.

Abstract: A computerized method of video analysis that includes receiving several series of video frames generated by a number of image sensors. Each image sensor has its own field-of-view which can, but does not have to, overlap with another image sensor's field-of-view. The image sensors monitor a portion of a monitored environment. The computerized method also includes concurrently tracking, independent of calibration, multiple objects within the monitored environment as the objects move between fields-of-view, and multiple objects within one field-of-view.

Abstract: A computerized method of video analysis including receiving image data for a plurality of video frames depicting a scene that includes at least one of a plurality of background features. Each video frame includes s a plurality of image regions and at least one video frame has an object within at least one of the image region. A plurality of background classifications is provided that correspond to one of the background features in the scene. At least one image region is assigned a background classification based at least in part on the location of the object relative to the image region.