Abstract: Aspects of the invention provide a method of face recognition that includes receiving plural frames of a video stream imaging a candidate individual, e.g., in the field of view of a camera, and generating for each of those frames a score of the image and/or of the candidate therein. This can include a score (or count) indicative of the number of individuals present in the frame, a pose of the candidate individual (e.g., face-on or otherwise), blur in the image, and so forth. The method further includes selecting, based on the respective scores of the frames, a subset of the frames for matching by a face recognizer against a set of one or more images of designated individuals. That set may be of individuals approved for access, individuals to be prevented for access, or otherwise. An output can be generated based on such matching by the face recognizer.

Abstract: Aspects of the invention provide, in some aspects, a method of face recognition that includes receiving plural frames of a video stream imaging a candidate individual, e.g., in the field of view of a camera, and generating for each of those frames a score of the image and/or of the candidate therein. This can include, for example, a score (or count) indicative of the number of individuals present in the frame, a pose of the candidate individual (e.g., face-on or otherwise), blur in the image, and so forth. The method further includes selecting, based on the respective scores of the frames, a subset of the frames for matching by a face recognizer against a set of one or more images of designated individuals. That set may be of individuals approved for access, individuals to be prevented for access, or otherwise. An output is generated, according to the method, based on such matching by the face recognizer.

Abstract: A system uses facial recognition to exclude persons of interest (e.g., “undesirables”) from events and/or venues. Such a system can include a combination of cameras, edge processing devices, and servers that are in communications coupling (e.g., via a network) and that are on premise and/or in the cloud to recognize such persons of interest (POI) and interdict and prevent such POI from entering, traversing, and/or attending (collectively, “entering”) such venues or events.

Abstract: Aspects of the invention provide a vision pipeline-based method of censusing a crowd that includes presenting content on an outdoor digital display or other content player (e.g., a loudspeaker) and capturing with a video camera or other image acquisition device frames or other time-wise succession of images of a scene in the field of view or otherwise in the vicinity of the display/player. First and second people detectors (both, for example, face detectors) are used to determine respective counts of persons in the scene. Estimated viewing statistics, generated with a detection pipeline that includes the first people detector and a tracker, include at least one of (i) a number of persons in the scene that viewed the content on the player, and (ii) for at least one of those persons, a duration during which he/she was in the scene and/or looking toward the player.

Abstract: The invention provides, in some respects, a digital data processing method of identifying a person of interest in a video stream. The method, which executes on a digital data processing system including one or more digital data processing devices in communications coupling with one another, includes detecting using a detector and without using a facial recognizer one or more faces in a video stream. It further includes using a tracker to associate, without using a facial recognizer, one or more of the detected faces with a same apparent individual, and scoring, again, without using a facial recognizer, each of the detected faces that are associated with the same apparent individual. The method further includes applying selected ones of the scored detected faces of the same apparent individual to a facial recognizer to identify a person of interest, where selection is based on scores of the detected faces.

Abstract: The system and methods disclosed herein include a runtime architecture that takes a nonspecific set of systems of differential equations, distributes them across the network, and iteratively integrates them through time with a possibility to output the results on every iteration. Embodiments of the disclosed system may be used for neural computation or any other suitable application. Embodiments can be used as a standalone engine or as part of another computational system for massively parallel numerical integration of a data-driven dynamical system.

Abstract: A system uses facial recognition to exclude persons of interest (e.g., “undesirables”) from events and/or venues. Such a system can include a combination of cameras, edge processing devices, and servers that are in communications coupling (e.g., via a network) and that are on premise and/or in the cloud to recognize such persons of interest (POI) and interdict and prevent such POI from entering, traversing, and/or attending (collectively, “entering”) such venues or events.

Abstract: The system and methods disclosed herein include a runtime architecture that takes a nonspecific set of systems of differential equations, distributes them across the network, and iteratively integrates them through time with a possibility to output the results on every iteration. Embodiments of the disclosed system may be used for neural computation or any other suitable application. Embodiments can be used as a standalone engine or as part of another computational system for massively parallel numerical integration of a data-driven dynamical system.

Abstract: The system and methods disclosed herein include a runtime architecture that takes a nonspecific set of systems of differential equations, distributes them across the network, and iteratively integrates them through time with a possibility to output the results on every iteration. Embodiments of the disclosed system may be used for neural computation or any other suitable application. Embodiments can be used as a standalone engine or as part of another computational system for massively parallel numerical integration of a data-driven dynamical system.

Abstract: Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on “stovepiped,” or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements.

Abstract: Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on “stovepiped,” or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements.

Abstract: The system and methods disclosed herein include a runtime architecture that takes a nonspecific set of systems of differential equations, distributes them across the network, and iteratively integrates them through time with a possibility to output the results on every iteration. Embodiments of the disclosed system may be used for neural computation or any other suitable application. Embodiments can be used as a standalone engine or as part of another computational system for massively parallel numerical integration of a data-driven dynamical system.

Abstract: Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on “stovepiped,” or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements.