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 system and method for performing automated defect detection by utilizing data from prior inspections is disclosed. The system and method may include providing a image capture device for capturing and transmitting at least one current image of an object and providing a database for storing at least one prior image from prior inspections. The system and method may further include registering the at least one current image with the at least one prior image, comparing the registered at least one current image with the at least one prior image to determine a transformation therebetween and updating the database with the at least one current image.

Abstract: A system and method for performing automated defect detection of blades within an engine is disclosed. The system and method may include an image capture device capable of capturing and transmitting images of a plurality of blades of an engine, creating a normal blade model of an undamaged one of the plurality of blades and determining defects within the plurality of blades by utilizing the normal blade model.

Abstract: A system and method for improving human-machine interface while performing automated defect detection is disclosed. The system and method may include an image capture device for capturing and transmitting data of an object, performing automated analysis of the data and reviewing results of the automated analysis by a human inspector and providing feedback. The system and method may further include refining the automated analysis of the data based upon the feedback of the human inspector.

Abstract: A computer program product and method for performing automated defect detection of blades within an engine is disclosed. The method may include providing a storage medium for storing data and programs used in processing video images, providing a processing unit for processing images, receiving from a borescope an initial set of images of a plurality of members inside of a device, and using the processing unit to apply Robust Principal Component Analysis to decompose the initial set of images into a first series of low rank component images and a second series of sparse component images, wherein there are at least two images in the initial series.

Abstract: A computer program product and method for performing position control on device members that have been identified as defective. The method may include providing a storage medium that stores data and programs used in processing images and a processing unit that processes the images, receiving, by the processing unit from an image capture device coupled to the processing unit, a set of images of a plurality of members inside of a device, detecting, by the processing unit, a defect in a first member of the plurality of members, and providing instructions to move the first member to an inspection position in the device. The device may be an engine and the members may be blades within the engine.

Abstract: A system and method for performing automated defect detection using multiple image capture devices is disclosed. The system and method may include providing a plurality of image capture devices, the plurality of image capture devices capturing and transmitting a plurality of images of an object. The system and method may further include determining a feature correspondence between the plurality of images of the plurality of image capture devices, creating mosaiced images of the plurality of images if the feature correspondence is found or known and performing at least of an automated analysis and a manual inspection on the mosaiced images to find any defects in the object.

Abstract: A method for detecting flame and smoke using spatial analysis of video input (40) provided by a video detector. The video input (40) consists of a number of individual frames, wherein analysis is performed on each individual frame to detect and outline regions that may contain smoke or flame (42). Based on the defined outline or boundary of each detected region, spatial features associated with the region are extracted (52), such as perimeter/area and surface area/volume. The extracted spatial features are related to one another (54) to determine the likelihood that the region contains smoke or flame. Extracted spatial features may be related to one another using a power law relationship that provides an indication of the turbulence associated with a bounded region, wherein turbulence is a characteristic of both flame and smoke.

Abstract: An auto-commissioning system provides automatic parameter selection for an intelligent video system based on target video provided by the intelligent video system. The auto-commissioning system extracts visual feature descriptors from the target video and provides the one or more visual feature descriptors associated with the received target video to an parameter database that is comprised of a plurality of entries, each entry including a set of one or more stored visual feature descriptors and associated parameters tailored for the set of stored visual feature descriptors. A search of the parameter database locates one or more best matches between the extracted visual feature descriptors and the stored visual feature descriptors. The parameters associated with the best matches are returned as part of the search and used to commission the intelligent video system.

Abstract: A video enhancement system provides automatic enhancement to video data that includes the presence of obscurations. The system provides near-field enhancement of the video data by detecting the presence of near-field obscurations such as snowflakes/raindrops and determining whether the detected obscurations are located over background pixels or foreground objects. The detected obscuration pixels are filled-in depending on whether they are located over the background or foreground to create an enhanced image. The system may also provide local/global adaptive contrast enhancement to enhance video in the presence of far-field obscurations.

Abstract: A closed-loop system employs video analytic outputs in a feedback loop to control the operation of a video-based fire detection system. In particular, video data captured by a video detector is analyzed by a video analytic system to generate outputs identifying regions indicative of fire. These outputs are employed as feedback in a closed-loop control system to orient the camera such that field of the view of the camera is modified to improve the ability of the video analytic system to verify or confirm the presence of fire within a region identified as indicative of fire. In addition, video analytic system may generate outputs identifying the delivery location of a fire suppressant. These outputs are employed as feedback in a closed-loop control system to orient the delivery of suppressant to extinguish the fire.

Abstract: An elevator control system (24) provides elevator dispatch and door control based on passenger data received from a video monitoring system. The video monitoring system includes a video processor (16) connected to receive video input from at least one video camera (12). The video processor (16) tracks objects located within the field of view of the video camera, and calculates passenger data parameters associated with each tracked object. The elevator controller (24) provides elevator dispatch (26), door control (28), and security functions (30) based in part on passenger data provided by the video processor (16). The security functions may also be based in part on data from access control systems (14).

Abstract: An alarm filter (22) for use in a security system (14) to reduce the occurrence of nuisance alarms receives sensor signals (S1-Sn, Sv) from a plurality of sensors (18, 20) included in the security system (14). The alarm filter (22) produces an opinion output as a function of the sensor signals and selectively modifies the sensor signals as a function of the opinion output to produce verified sensor signals (S1?-Sn?).

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.

Abstract: A video content analysis (VCA) system generates an output regarding a detected condition that provides an indication of a confidence level regarding the detected condition. One example VCA system determines whether a first characteristic of a detected object in a field of vision of the video content analysis system satisfies a first criterion. If so, a first signal is generated under selected conditions. The VCA system also determines whether a second characteristic of the detected object satisfies a corresponding second criterion. If so, a second, different signal is generated if the first and second criteria are satisfied. The first and second signals indicate respective, different confidence levels that an event has occurred. A disclosed example includes a VCA as part of a security system.

Abstract: A method for detecting flame and smoke using spatial analysis of video input (40) provided by a video detector. The video input (40) consists of a number of individual frames, wherein analysis is performed on each individual frame to detect and outline regions that may contain smoke or flame (42). Based on the defined outline or boundary of each detected region, spatial features associated with the region are extracted (52), such as perimeter/area and surface area/volume. The extracted spatial features are related to one another (54) to determine the likelihood that the region contains smoke or flame. Extracted spatial features may be related to one another using a power law relationship that provides an indication of the turbulence associated with a bounded region, wherein turbulence is a characteristic of both flame and smoke.