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

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 video surveillance system comprises a first sensing unit; a second sensing unit; and a communication medium connecting the first sensing unit and the second sensing unit. The first sensing unit provides information about a position of a target to the second sensing unit via the communication medium, and the second sensing unit uses the position information to locate the target.

Abstract: A video camera may overlook a monitored area from any feasible position. An object flow estimation module monitor the moving direction of the objects in the monitored area. It may separate the consistently moving objects from the other objects. A object count estimation module may compute the object density (e.g. crowd). A object density classification module may classify the density into customizable categories.

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 system for video monitoring a retail business process includes a video analytics engine to process video obtained by a video camera and generate video primitives regarding the video, A user interface is used to define at least one activity of interest regarding an area being viewed, each activity of interest identifying at least one of a rule or a query regarding the area being viewed. An activity inference engine processes the generated video primitives based on each defined activity of interest to determine if an 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.

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 technique for video processing includes: receiving video from a scene; detecting moving pixels in the video; detecting line segments or motion blocks in the video based on the detected moving pixels; identifying targets in the video based on the detected line segments or motion blocks; tracking targets in the video based on the identified targets; and managing the tracked targets in the video.

Abstract: Video processing to detect a stationary object in a video includes: performing background change detection on the video; performing motion detection on the video; determining stable pixels in the video based on the background change detection; and combining the stable pixels to identify at least one stationary object in the video.

Abstract: A method of operating a video surveillance system is provided. The video surveillance system including at least two sensing units. A first sensing unit having a substantially 360 degree field of view is used to detect an event of interest. Location information regarding a target is sent from the first sensing unit to at least one second sensing unit when an event of interest is detected by the first sensing unit.

Abstract: Detection of an omitted process in an event having of a sequence of processes includes: receiving video of an action area; receiving transaction data regarding a transaction occurring at the action area; detecting at least two different actor motion states in the video; detecting an event based on the motion states; and detecting the omitted process based on the detected event and the transaction data.

Abstract: A video surveillance 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: 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 analysis may comprise tracking a state of each target in a video through multiple frames of said video, each state indicating a visibility condition of a target.

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.