An embodiment includes determining an experiential state of a first user participating in a mixed-reality experience. The embodiment also includes creating a first driver model that maps a relationship between the experiential state of the first user and a parameter of the mixed-reality experience. The embodiment also includes aggregating the first driver model with a plurality of driver models associated with experiential states and parameters of respective other users. The embodiment also includes creating a first cohort experience model using the aggregated driver models. The embodiment also includes deriving a first cohort experience parameter for the first cohort experience model. The embodiment also includes initiating an automated remedial action for participants in the mixed-reality system being associated with the first cohort experience model and the first cohort experience parameter.

Embodiments of the present disclosure disclose an anomaly detector for detecting an anomaly in a sequence of poses of a human performing an activity. The anomaly detector includes an input interface configured to accept input data indicative of a distribution of the sequence of poses, a memory configured to store a discriminative one-class classifier having a pair of complementary classifiers bounding normal distribution of pose sequences in a reproducing kernel Hilbert space (RKHS), a processor configured to embed the input data into an element of the RKHS and classify the embedded data using the discriminative one-class classifier, and an output interface configured to render a classification result.

A system receives a digital representation of a biometric for a person, uses the digital representation of the biometric to determine and/or otherwise retrieve identity information associated with the person, and uses the identity information to perform one or more actions related to the person's presence in one or more areas. For example, the system may estimate a path for the person and signal an agent electronic device based on the path. In another example, the system may determine a presence of a person within the area and/or transmit information to an agent electronic device regarding the determined presence. In still another example, the system may receive a request to communicate with the person and forward the communication to the person using the identity information.

An information processing apparatus (100) includes an acquisition unit (122) that acquires a first image from which person region feature information regarding a region including other than a face of a retrieval target person is extracted, a second image in which a collation result with the person region feature information indicates a match, and a facial region is detected, and result information indicating a collation result between face information stored in a storage unit and face information extracted from the facial region, and a display processing unit (130) that displays at least two of the first image, the second image, and the result information on an identical screen.

A system receives a digital representation of a biometric for a person, uses the digital representation of the biometric to determine and/or otherwise retrieve identity information associated with the person, and uses the identity information to perform one or more actions related to the person's presence in one or more areas. For example, the system may estimate a path for the person and signal an agent electronic device based on the path. In another example, the system may determine a presence of a person within the area and/or transmit information to an agent electronic device regarding the determined presence. In still another example, the system may receive a request to communicate with the person and forward the communication to the person using the identity information.

Systems and methods for effectively repelling pest animals (e.g., birds), including drones that adopt complex deterrent strategies (e.g., cooperative strategies), establishing a fuzzy boundary for a geofenced area and altering pest deterrent device flight patterns based on the characteristics of the fuzzy boundaries. Deterrence strategies can be selected based on the type of pest animals, and new deterrence strategies can be generated based on outcome feedback from previous strategies (e.g., combining aspects of preexisting deterrence strategies by utilizing an AI system). Drones can be automatically maintained by comparing current drone operational status with a predetermined threshold level. A maintenance robot (e.g., a drone) can autonomously rescues a working robot (e.g., another drone) that is in trouble.

A system receives a digital representation of a biometric for a person, uses the digital representation of the biometric to determine and/or otherwise retrieve identity information associated with the person, and uses the identity information to perform one or more actions related to the person's presence in one or more areas. For example, the system may estimate a path for the person and signal an agent electronic device based on the path. In another example, the system may determine a presence of a person within the area and/or transmit information to an agent electronic device regarding the determined presence. In still another example, the system may receive a request to communicate with the person and forward the communication to the person using the identity information.

A collation apparatus 1 includes: a vector-type arithmetic unit 2 that calculates first similarity degrees using first feature points extracted from a target biological image and second feature points in a plurality of registered biological images, and narrows down the registered biological images based on the calculated first similarity degrees; and an arithmetic unit 3, other than the vector-type arithmetic unit 2, that calculates second similarity degrees using third feature points extracted from the target biological image and fourth feature points in the registered biological images obtained by the narrowing-down, and specifies a registered biological image based on the calculated second similarity degrees.

One or more sensors are configured for detection of characteristics of moving objects and living subjects for human identification or authentication. One or more processors, such as in a system of sensors or that control a sensor, may be configured to process signals from the one or more sensors to identify a person. The processing may include evaluating features from the signals such as breathing rate, respiration depth, degree of movement and heart rate etc. The sensors may be radio frequency non-contact sensors with automated detection control to change detection control parameters based on the identification of living beings, such as to avoid sensor interference.

This application discloses a method for training a gaze tracking model, including: obtaining a training sample set; processing the eye sample images in the training sample set by using an initial gaze tracking model to obtain a predicted gaze vector of each eye sample image; determining a model loss according to a cosine distance between the predicted gaze vector and the labeled gaze vector for each eye sample image; and iteratively adjusting one or more reference parameters of the initial gaze tracking model until the model loss meets a convergence condition, to obtain a target gaze tracking model. According to the solution provided in this application, a gaze tracking procedure is simplified, a difference between a predicted value and a labeled value can be better represented by using the cosine distance as a model loss to train a model, to improve prediction accuracy of the gaze tracking model.