Abstract: A built-in fault-detection-and-isolation (FDI) test for a system that has measurable input operating conditions and output parameters is designed. Inferential sensors, which are functional combinations of the input operating conditions and the output parameters, are evolved using genetic programming so as to be rich in information pertaining to fault conditions of the system. Simulations, based on a system model, of various combinations of the input operating conditions and the fault conditions are performed so as to provide simulated values of the inferential sensors and the output parameters. Sensitivities of the inferential sensors and the output parameters to the fault conditions and to system uncertainties are calculated. The inferential sensors are repeatedly evolved until a termination condition is achieved. The built-in test is designed based on a combination of a selected input operating condition and one or more of the inferential sensors and/or the output parameters.

Abstract: A video recognition system detects the presence of fire based on video data provided by one or more video detectors, but suppresses the triggering of an alarm in situations based on the selection of acceptable regions and application of rules associated with each acceptable region. A user defines acceptable regions within the field of view of the video detector and associates with each acceptable region a rule. During processing of video data associated with the field of view, video metrics are calculated and analyzed to detect the presence of fire (e.g., flame or smoke). Prior to triggering an alarm, regions identified as indicative of fire are compared with the user-defined acceptable regions. If there is overlap between the two regions, the rule associated with the acceptable region is applied to determine whether the alarm should be suppressed or triggered.

Abstract: A video-based fire detection system receives video data comprised of a plurality of individual frames, and determines based on the video data the ability of the system to detect the presence of fire. The system includes a video recognition system connectable to receive the video data and to calculate one or more background features associated with the video data. Based on the calculated background features, the video recognitions system assesses the ability of the video-based fire detection system to detect the presence of fire. The system includes one or more outputs operably connectable to communicate the results of the assessment made by the video recognitions system.

Abstract: An exemplary security device includes a controller that determines whether a security credential for an individual corresponds to an authorized credential. The controller also determines whether an acquired image of the individual corresponds to a reference image that is associated with the authorized credential. The controller determines if a correspondence between the acquired image and the reference image satisfies a selected criterion when the security credential corresponds to the authorized credential. The controller updates the reference image responsive to the selected criterion being satisfied. The controller updates the reference image by including the acquired image into the reference image and weighting the acquired image higher than other image information previously incorporated into the reference image.

Abstract: A video recognition system detects the presence of fire based on video data provided by one or more video detectors. The video recognition system divides each individual frame of video data into a plurality of frame elements and calculates with respect to each frame element a structural feature that the algorithm uses to identify a core region of potential fire. The video recognition system defines a region surrounding the core region and calculates with respect to each frame element within the surrounding region a temporal feature. Based on the calculated temporal feature, the video recognition system associates a representative temporal value with the core region. Based on the structural and temporal features associated with the core region, video recognition system determines whether the video data indicates the presence of fire.

Abstract: A video recognition system detects the presence of fire based on video data provided by one or more video detectors, but suppresses the triggering of an alarm in situations based on the selection of acceptable regions and application of rules associated with each acceptable region. A user defines acceptable regions within the field of view of the video detector and associates with each acceptable region a rule. During processing of video data associated with the field of view, video metrics are calculated and analyzed to detect the presence of fire (e.g., flame or smoke). Prior to triggering an alarm, regions identified as indicative of fire are compared with the user-defined acceptable regions. If there is overlap between the two regions, the rule associated with the acceptable region is applied to determine whether the alarm should be suppressed or triggered.

Abstract: A video recognition system detects the presence of fire based on video data provided by one or more video detectors. The video recognition system divides each individual frame of video data into a plurality of frame elements and calculates with respect to each frame element a structural feature that the algorithm uses to identify a core region of potential fire. The video recognition system defines a region surrounding the core region and calculates with respect to each frame element within the surrounding region a temporal feature. Based on the calculated temporal feature, the video recognition system associates a representative temporal value with the core region. Based on the structural and temporal features associated with the core region, video recognition system determines whether the video data indicates the presence of fire.

Abstract: A video recognition system detects the presence of fire based on video data provided by one or more video detectors. The video recognition system is operable to calculate a first flicker feature with respect to a first set of frame data and a second flicker feature with respect to a second set of frame data. The video recognition system combines the first flicker feature and the second flicker feature to generate an accumulated flicker feature. The video recognition system defines, based on the accumulated flicker feature, a flicker mask that represents a dynamic region of the fire. Based on the defined flicker mask, the video recognition system determines whether the video data indicates the presence of fire.

Abstract: A video-based fire detection system receives video data comprised of a plurality of individual frames, and determines based on the video data the ability of the system to detect the presence of fire. The system includes a video recognition system connectable to receive the video data and to calculate one or more background features associated with the video data. Based on the calculated background features, the video recognitions system assesses the ability of the video-based fire detection system to detect the presence of fire. The system includes one or more outputs operably connectable to communicate the results of the assessment made by the video recognitions system.