Ncr Corporation

Emtera Corporation

Ralph N Crabtree

Michael C Moed

Mehdi Khosravi

Jianzhong Huang

Antai Peng

Jorge E Perez-Jacome

Junhoy Kim

A system and method for tracking movement of objects in a scene from a stream of video frames using first and second correspondence graphs. A first correspondence graph, called an object correspondence graph, is formed comprising a plurality of nodes representing region clusterss in the scene which are hypotheses of objects to be tracked, and a plurality of tracks. Each track comprises an ordered sequence of nodes in consecutive video frames that represents a track segment of an object through the scene. A second correspondence graph, called a track correspondence graph, is created, comprising a plurality of nodes, each node corresponding to at least one track in the first correspondence graph. A track comprising an ordered sequence of nodes in the second correspondence graph represents the path of an object through the scene. Tracking information for objects, such as persons, in the scene, is accumulated based on the first correspondence graph and second correspondence graph.

System and method for tracking movement of objects in a scene using correspondence graphs

A system and method for tracking movement of objects, such as people, through a scene. A background image comprising image information representing a background of the scene is generated. Image information for a video frame is compared with image information of the background image to generate regions in a video frame which potentially represent objects to be tracked in the scene. Region clusters are formed by combinations of regions if such combination satisfy predetermined criteria, and the region clusters are hypotheses of objects to be tracked. Each region cluster is evaluated to determine whether each represents an object to be tracked, based on object model information and correspondence to region clusters in a prior video frame. A confidence value is generated for each region cluster that represents a likelihood that the region represents an object to be tracked. A correspondence is determined between region clusters in consecutive video frames. Tracks of objects through the scene over time are constructed based upon the correspondence determined between region clusters in consecutive video frames.

System and method for tracking movement of objects in a scene

A system and method for detecting hand and item movement patterns comprising a video camera positioned to view a scene which includes therein a scanner for scanning items, wherein the video camera generates a sequence of video frames representing activity in the scene, processing means coupled to the video camera, the processing means performing steps of identifying regions of a video frame representing a hand; and tracking hand movement with respect to the scanner over a plurality of video frames. Event information descriptive of user activity at the self-service checkout terminal is generated based on the tracking information.

Method and apparatus for detecting movement patterns at a self-service checkout terminal

A tracking system and method for evaluating whether image information for a region cluster of a video frame of a scene represents an hypothesis of an object to be tracked, such as a person. At least one real-world feature of a region cluster corresponding to an object to be tracked is generated. For example, the at least feature is at least one possible location of a predetermined portion of an object represented by the region cluster is determined based on a viewing angle of the scene of the video camera. A distance from the video camera to the object corresponding to the region is determined in real-world coordinates for each possible location of the predetermined portion of the region cluster. Real-world size and location information for the region cluster is determined based on the distance. The real-world size and location information for the region cluster is compared with statistical information for the particular type of object to determine a confidence value representing a measure of confidence that the region cluster represents the particular type of object.

System and method for matching image information to object model information

A system and method for extracting image information from a video frame for regions of a the video frame that likely are objects of interest in a scene. An initial region set is generated by comparing luminance image information and color image information of a video frame with luminance image information and color image information of a background image for the scene. A high confidence region set is generated comprising regions from the initial based upon edge information of the regions and edge information in the background image. A final region set is generated by combining one or more regions in the high confidence region set if such combinations satisfy predetermined criteria, including size, region proximity and morphological region dilation.

System and method for segmenting image regions from a scene likely to

A multi-resolution background adaptation system and method wherein image information for a video frame is analyzed to update a background image in as great a resolution as possible. The video frame is partitioned into a plurality of cell levels, each cell level comprising at least one cell. Each cell is further partitioned into children cells for several hierarchical levels. Background image adaptation begins at the frame level and proceeds to lower cell levels of smaller cells when the lower cell levels are not substantially occluded by objects.

System and method for multi-resolution background adaptation

A knowledge-based document analysis system and method for identifying and decomposing constrained and unconstrained images of documents is disclosed. Low level features are extracted within bitonal and grayscale images. Low level features are passed to a document classification means which forms initial hypotheses about the document class. For constrained documents, the document analysis system sorts through various models to determine the exact type of document and then extracts the relevant fields for character recognition. For unconstrained documents, through the use of a blackboard architecture which includes a knowledge database and knowledge sources, the document analysis means creates information and hypotheses to identify and locate relevant fields within the document. These fields are then sent for optical character recognition.

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Atlanta, GA, United States of America

Ralph N Crabtree