Abstract: A video surveillance system is set up, calibrated, tasked, and operated. The system extracts video primitives and extracts event occurrences from the video primitives using event discriminators. The system can undertake a response, such as an alarm, based on extracted event occurrences.

Abstract: A video surveillance system is set up, calibrated, tasked, and operated. The system extracts video primitives and extracts event occurrences from the video primitives using event discriminators. The system can undertake a response, such as an alarm, based on extracted event occurrences.

Abstract: A video sequence is encoded, where the video sequence includes a background composite and foreground regions. The video sequence is encoded based on balancing bits per pixel for the background composite with bits per pixel for the foreground regions.

Abstract: A method for segmenting video data into foreground and background portions utilizes statistical modeling of the pixels. A statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons. The method for segmenting video data may be further incorporated into a method for implementing an intelligent video surveillance system.

Abstract: A decomposed original video sequence includes one or more original camera-motion layers and zero or more original fixed-frame layers decomposed from an original video sequence. The decomposed original video sequence is edited by editing at least one of the original camera-motion layers to obtain modified camera-motion layers such that each frame of a composite modified video sequence composed from the modified camera-motion layers and the original fixed-frame layers is obtained without editing each frame of said original video sequence. The editing comprises performing an edge operation to one of said original camera-motion layers.

Abstract: A method for segmenting video data into foreground and background portions utilizes statistical modeling of the pixels. A statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons. The method for segmenting video data may be further incorporated into a method for implementing an intelligent video surveillance system.

Abstract: A method of generating and utilizing a scene model from a sequence of video frames produces a three-dimensional scene model, useful for video processing. The method separates foreground and background data. It uses an estimate of relative motion of an observer to project each frame onto a coordinate system of the three-dimensional scene model. It then merges the background data of a given frame into the scene model.

Abstract: A method for implementing a video tripwire includes steps of calibrating a sensing device to determine sensing device parameters for use by the system; initializing the system, including entering at least one virtual tripwire; obtaining data from the sensing device; analyzing the data obtained from the sensing device to determine if the at least one virtual tripwire has been crossed; and triggering a response to a virtual tripwire crossing.

Abstract: A method for implementing a video tripwire includes steps of calibrating a sensing device to determine sensing device parameters for use by the system; initializing the system, including entering at least one virtual tripwire; obtaining data from the sensing device; analyzing the data obtained from the sensing device to determine if the at least one virtual tripwire has been crossed; and triggering a response to a virtual tripwire crossing.

Abstract: A method for segmenting video data into foreground and background portions utilizes statistical modeling of the pixels. A statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons.

Abstract: A method for extracting textual and graphical overlays from video sequences involves steps of detecting a potential overlay in a video sequence and then verifying that the potential overlay is an actual overlay. Detection of textual overlays involves wavelet decomposition and neural network processing, while detection of graphical overlays involves template matching. Verification of textual and graphical overlays involves spatial and/or temporal verification.

Abstract: A method for segmenting video data into foreground and background portions utilizes statistical modeling of the pixels. A statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons.