Abstract: A system, method and program product for providing automated learning for a people counting system. A system is disclosed that includes a grid system for dividing a field of view (FOV) of a captured image data into a set of blocks; an object detection and tracking system for tracking a blob passing through the FOV; and a learning system that maintains person size parameters for each block and updates person size parameters for a selected block when a blob appears in the selected block.

Abstract: A solution for monitoring an area uses color histograms and size information (e.g., heights and widths) for blob(s) identified in an image of the area and model(s) for existing object track(s) for the area. Correspondence(s) between the blob(s) and the object track(s) are determined using the color histograms and size information. Information on an object track is updated based on the type of correspondence(s). The solution can process merges, splits and occlusions of foreground objects as well as temporal and spatial fragmentations.

Abstract: An improved solution for detecting events in video is provided, in which a region of interest within a series of video images of the video is monitored. An object at least partially visible within the series of video images is tracked and a fiducial region of the object is identified. The fiducial region is one or more points and/or area(s) of the object, which are relevant in determining whether an alert should be generated. The fiducial region is monitored with respect to the region of interest and a restricted behavior. When the restricted behavior is detected with respect to the region of interest, an alert is generated.