Abstract: A method of video processing may include analyzing input video information to determine if a current video frame is directed to a same view as a previous video frame; determining whether a new view is present; and indicating a need to use video processing information pertaining to the new view if a new view is determined to be present.

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 extracted video primitives and event occurrences may be used to create and define additional video analytic rules. 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 passback direction for a video monitored area may be defined. Video collected from the video monitored area may be accessed. Video collected from a video monitored area may be analyzed based on a passback direction of the video monitored area to detect any passback events in the video monitored area. An action based on the detected passback events may be initiated.

Abstract: A method for segmenting video data into foreground and background (324) portions utilizes statistical modeling of the pixels ? statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared (326) 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 The method for segmenting video data may be implemented in hardware.

Abstract: Input video data is processed to detect a change in a posture of a person shown in the video data. The change of posture may be the result of an event, for example, the person falling or getting up. The input video data may include a plurality of frames. Objects in the frames are tracked and then classified, for example, as human and non-human targets. At least one of the position or location of a human target in the frames is identified. Changes in the location or position of the human target between the frames is determined. When the change in at least of the position or location exceeds a predetermined threshold, a falling down event or a getting up event is detected. The changes in the position or location of the human target can be determined based on a number of different factors.

Abstract: A method of video surveillance may include performing on input video at least one of the operations selected from the group consisting of motion detection and change detection, recording a motion pattern based on a result of said at least one of the operations, and analyzing the motion pattern to detect periodic motion in the video. A video surveillance apparatus may include a change detector, a motion detector, and/or a combination motion/change detector, a pattern analyzer, and a periodic motion detector.

Abstract: A computer-readable medium contains software that, when read by a computer, causes the computer to perform a method for wide-area site-based surveillance.

Abstract: Video content analysis of a video may include: modeling a background of the video; detecting at least one target in a foreground of the video based on the feature blocks of the video; and tracking each target of the video. Modeling a background of the video may include: dividing each frame of the video into image blocks; determining features for each image block of each frame to obtain feature blocks for each frame; determining a feature block map for each frame based on the feature blocks of each frame; and determining a background feature block map to model the background of the vide based on at least one of the feature block maps.

Abstract: A sequence of video frames of an area of interest is obtained. A first background model of the area of interest is constructed based on a first parameter. A second background model of the area of interest is constructed based on a second parameter, the second parameter being different from the first parameter. A difference between the first and second background models is determined. A stationary target is determined based on the determined difference. An alert concerning the stationary target is generated.

Abstract: A computer-based system for performing scene content analysis for human detection and tracking may include a video input to receive a video signal; a content analysis module, coupled to the video input, to receive the video signal from the video input, and analyze scene content from the video signal and determine an event from one or more objects visible in the video signal; a data storage module to store the video signal, data related to the event, or data related to configuration and operation of the system; and a user interface module, coupled to the content analysis module, to allow a user to configure the content analysis module to provide an alert for the event, wherein, upon recognition of the event, the content analysis module produces the alert.

Abstract: A computer-based method for automatic detection of water regions in a video include the steps of estimating a water map of the video and outputting the water map to an output medium, such as a video analysis system. The method may further include the steps of training a water model from the water map; re-classifying the water map using the water model by detecting water pixels in the video; and refining the water map.

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. The method for segmenting video data may be implemented in hardware.

Abstract: A machine-accessible medium containing software code that, when read by a computer, causes the computer to perform method for filtering a video in a video system. The method may include generating a foreground mask and a background model from a video, where the foreground mask comprises moving pixels in the video and the background model comprises a statistical description, including a mean and a variance value, for each pixel in the video. Then the background model variance is filtered with a one-dimensional high pass filter in a single orientation. Next, for each linear grouping of pixels in the single orientation of the filtered variance, a first edge pixel is detected between a high-variance group of pixels and a low-variance group of pixels. Then a first waterline position is detected as an area bounded by the first edge pixels.

Abstract: A system for detecting behavior of a target may include: a target detection engine, adapted to detect at least one target from one or more objects from a video surveillance system recording a scene; a path builder, adapted to create at least one mature path model from analysis of the behavior of a plurality of targets in the scene, wherein the at least one mature path model includes a model of expected target behavior with respect to the at least one path model; and a target behavior analyzer, adapted to analyze and identify target behavior with respect to the at least one mature path model. The system may further include an alert generator, adapted to generate an alert based on the identified behavior.

Abstract: A system detects an object in frames of a video sequence to obtain a detected object, tracks the detected object in the frames of the video sequence to obtain a tracked object, and classifies the tracked object as a real object or a spurious object based on spatial and/or temporal properties of the tracked object.

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 is obtained. The obtained video is transmitted. An advertising content is provided. The transmitted video is received. A background from the video is segmented. The segmented background is replaced with the advertising content. The video with the replaced background is rendered on a monitor.

Abstract: A system for video monitoring at least one banking business process may comprise a video analytics engine to process video of a bank area obtained by a video camera and to generate video primitives regarding the video; a user interface to define at least one activity of interest regarding the bank area being viewed, wherein each activity of interest identifies a rule and/or a query regarding the bank area being viewed; and an activity inference engine to process the video primitives according to a banking business process, based on each activity of interest from the user interface to determine if any activity of interest occurred in the video.

Abstract: Non video data regarding a point of sale (POS) transaction is received. POS primitives are generated based on the received non video POS data. Video data regarding a corresponding POS transaction is received. Video primitives are generated based on the received video data. An exceptional transaction is inferred based on a corresponding exceptional transaction rule and at least one of the generated POS primitives or video primitives.