Abstract: A method to determine a base focal length of a pan-tilt-zoom (PTZ) camera system. The method includes generating a spherical mosaic with a feature track by the camera system and determining, by the camera system, a base focal length by analyzing a conic trajectory created by the feature track on an image plane.

Abstract: A system and method of analyzing a plurality of video signals received concurrently from a plurality of video cameras includes receiving a plurality of video signals captured from a plurality of respective video cameras, each video signal received over a respective camera channel; multiplexing the plurality of video signals to output a multiplexed video signal including frames from each of the captured video signals; performing automated video content analysis on the multiplexed video signal, including performing automated video content analysis for at least two of the camera channels.

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: A system and method for video surveillance and searching are disclosed. Video is analyzed and events are automatically detected. Based on the automatically detected events, textual descriptions are generated. The textual descriptions may be used to supplement video viewing and event viewing, and to provide for textual searching for events.

Abstract: A sensing device includes: a video imager to obtain a video; a processing unit to receive and process the video from the video imager; and a communication channel to output non-imagery signals.

Abstract: In one exemplary embodiment a method calibrates a pan-tilt-zoom (PTZ) camera system. The PTZ camera system may include a PTZ camera. The method may perform at least one of the following steps: determining a radial distortion of the PTZ camera; determining a base focal length of the PTZ camera; determining a zoom and magnification profile of the PTZ camera; determining an actuation delay of the PTZ camera system; determining a pan position mode speed and/or a tilt position mode speed of the PTZ camera; determining a pan velocity profile and/or a tilt velocity profile of the PTZ camera; determining a zoom position profile and/or a zoom duration profile of the PTZ camera; determining a query delay of the PTZ camera system; and determining a minimum query interval of the PTZ camera system.

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 sensing device includes: a video imager to obtain video of an area; a processing unit to receive and process the video from the video imager to identify one of an event or a datum of interest; and a communication channel to output a non-imagery signal representing the event or datum.

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 video imager may be used to measure the natural and/or artificial light levels in a space. The lighting estimates may be passed to a controller via a communication network. The lighting estimates may be in the form of actual radiance values, brightness, or other forms. The controller may determine if and which areas of the space require more light or if and which areas of the space have more light than required. Based on the lighting estimates, the controller may issue commands to turn on/off or dim/brighten the light from various ones or combinations of light sources in or around the space via actuators. The controller may also directly interface with the light sources.

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 method of monitoring a point of sale (POS) transaction including: receiving non video data regarding the POS transaction; defining exceptional transaction rules based at least on predetermined non video POS data events; inferring a potentially exceptional transaction based on a corresponding exceptional transaction rule and the received POS non video data; and verifying the inferred potentially exceptional transaction with video data regarding a corresponding POS transaction.

Abstract: A video sequence of a field of view within an environment is received. Targets are detected in the video sequence. Target geo-positional information is received. Correspondences between the targets detected in the video sequence and the target geo-positional information are determined and used to calibrate the camera and to geo-register a field of view of the camera.

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: 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 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: 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 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 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 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.