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 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 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: 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: 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: 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 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 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 method of video processing may include registering one or more frames of input video received from a sensing unit, where the sensing unit may be capable of operating in a scanning mode. The registration process may project the frames onto a common reference. The method may further include maintaining a scene model corresponding to the sensing unit's field of view. The method may also include processing the registered frames using the scene model, where the result of processing the registered frames includes visualization of at least one result of processing.

Abstract: A video surveillance system may include at least one sensing unit capable of being operated in a scanning mode and a video processing unit coupled to the sensing unit, the video processing unit to receive and process image data from the sensing unit and to detect scene events and target activity.

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: A video-based human, non-human, and/or motion verification system and method may include a video sensor adapted to obtain video and produce video output. The video sensor may include a video camera. The video-based human verification system may further include a processor adapted to process video to verify a human presence, a non-human presence, and/or motion. An alarm processing device may be coupled to the video sensor, the alarm processing device being adapted to receive video output or alert information from the video sensor.

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.

Abstract: A method of video surveillance may include pre-processing video data to obtain foreground edge information for at least one frame of said video data. The method may perform line segment detection on said foreground edge information to obtain one or more line segments. The line segment detection may be performed by means of an algorithm in which edge pixels are searched and line segments found are checked for validity. It may detect and track one or more targets based on said one or more line segments and may determine if at least one predetermined event is present in said at least one frame of said video data based on the detecting and tracking of said one or more targets.

Abstract: A video-based human verification system and method may include a video sensor adapted to obtain video and produce video output. The video sensor may include a video camera. The video-based human verification system may further include a processor adapted to process video to verify a human presence. An alarm panel may be coupled to the video sensor, the alarm panel being adapted to receive video output or alert information from the video sensor.

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: An input video sequence may be processed by processing the input video sequence to obtain target information; and building at least one target property map based on said target information. The target property map may be used to detect various events, particularly in connection with video surveillance.