Abstract:
A computer-implemented method for processing one or more video frames may include obtaining one or more video frames; generating one or more blobs using the one or more video frames; classifying the one or more blobs to produce one or more classified blobs, wherein the one or more classified blobs include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and constructing a list of detected targets based on the one or more classified blobs.
Abstract:
One or more video frames may be obtained, and a background model may be constructed based on a first parameter. A second background model may be constructed using the one or more video frames based on a second parameter, the second parameter being different from the first parameter. A difference between the first and second background models may be determined. One or more stationary targets may be determined based on the determined difference. The one or more stationary targets may be classified. An alert concerning the one or more classified stationary targets may be generated.
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:
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 method and system using face tracking and object tracking is disclosed. The method and system use face tracking, location, and/or recognition to enhance object tracking, and use object tracking and/or location to enhance face tracking.
Abstract:
A method and system using face tracking and object tracking is disclosed. The method and system use face tracking, location, and/or recognition to enhance object tracking, and use object tracking and/or location to enhance face tracking.
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 way further include an alert generator, adapted to generate an alert based on the identified behavior.
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 even 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 computer-implemented method for processing one or more video frames may include obtaining one or more video frames; generating one or more blobs using the one or more video frames; classifying the one or more blobs to produce one or more classified blobs, wherein the one or more classified blobs include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and constructing a list of detected targets based on the one or more classified blobs.