Intellivision Technologies Corporation

Nortek Security & Control LLC

Nice North America LLC

Intellivision

Krishna Khadloya

Vaidhi Nathan

Chandan Gope

Manuel Gonzalez

Albert Kay

Prakash Narayan

Ilya Popov

Semen Budenkov

Dmitry Yashunin

Gagan Gupta

Nitin Jindal

Maxim Sokolov

Sofiya Klyan

Person or object authentication can be performed using artificial intelligence-enabled systems. Reference information, such as for use in comparisons or assessments for authentication, can be updated over time to accommodate changes in an individual's appearance, voice, or behavior. In an example, reference information can be updated automatically with test data, or reference information can be updated conditionally, based on instructions from a system administrator. Various types of media can be used for authentication, including image information, audio information, or biometric information. In an example, authentication can be performed wholly or partially at an edge device such as a security panel in an installed security system.

Reference image enrollment and evolution for security systems

Human or object presence in or absence from a field-of-view of a camera can be achieved by analyzing camera data using a processor inside of or adjacent to the camera itself. In an example, a video signal processing system receives image data from one or more cameras and uses a processing circuit to determine whether a designated object is or is not present at a particular time, during a particular interval, or over a designated sequence of frames. In an example, the designated object can include one or more of a human being, a vehicle, or a parcel. In an example, results of the determination, such as including positive human or object identification, can be used as a trigger for operation of a barrier or access door.

Object detection in edge devices for barrier operation and parcel delivery

A function of a user-controlled virtual assistant (UCVA) device, such as a smart speaker, can be augmented using video or image information about an environment. In an example, a system for augmenting an UCVA device includes an image sensor configured to monitor an environment, a processor circuit configured to receive image information from the image sensor and use artificial intelligence to discern a presence of one or more known individuals in the environment from one or more other features in the environment. The system can include an interface coupled to the processor circuit and configured to provide identification information to the UCVA device about the one or more known human beings in the environment. The UCVA device can be configured by the identification information to update an operating mode of the UCVA device.

Event attendance monitoring using a virtual assistant

A security system can conditionally grant or deny access to a protected area using an artificial intelligence system to analyze images. In an example, an access control method can include receiving candidate information about a face and gesture from a first individual and receiving other image information from or about a second individual. The candidate information can be analyzed using a neural network-based recognition processor that can provide a first recognition result indicating whether the first individual corresponds to a first enrollee of the security system, and can provide a second recognition result indicating whether the second individual corresponds to a second enrollee of the security system. The example method can include receiving a passcode, such as from the first individual. Access can be conditionally granted or denied based on the passcode and the recognition results.

Multiple-factor recognition and validation for security systems

Artificial intelligence-based processing can be used to classify audio information received from an audio input unit. In an example, audio information can be received from a microphone configured to monitor an environment. A processor circuit can identify identifying one or more features of the audio information received from the microphone and use a first applied machine learning algorithm to analyze the one or more features and determine whether the audio information includes an indication of an abnormal event in the environment. In an example, the processor circuit can use a different second applied machine learning algorithm, such as a neural network-based deep learning algorithm, to analyze the same one or more features and classify the audio information as including an indication of a particular event type in the environment.

Audio type detection

Enrollment system with continuous learning and confirmation

Disclosed is a facial recognition system/method, utilizing modules to perform the following routine: extracting a feature descriptor from a detected feature point of a detected face in an input image frame; and matching the extracted feature descriptor with at least one of a pre-stored facial image that is index-mapped, comprising at least a first and second round of matching, wherein the second round of matching only selects the index-mapped facial images that matched above a pre-defined threshold from the first round of matching. Optionally, the above described steps may be coupled to a Point-of-Recognition (POR) provisioning, enabling an on-demand gate-keeping and/or payment processing for an end-user at an event/venue entry or point-of-sale.

Methods and systems for facial point-of-recognition (POR) provisioning

A license plate detection and recognition system receives training data comprising images of license plates. The system prepares ground truth data from the training data based predefined parameters. The system trains a first machine learning algorithm based on the ground truth data to generate a license plate detection model. The license plate detection model is configured to detect one or more regions in the images. The one or more regions contains a candidate for a license plate. The LPDR system generates a bounding box for each region. The LPDR system trains a second machine learning algorithm based on the ground truth data and the license plate detection model to generate a license plate recognition model. The license plate recognition model generates a sequence of alphanumeric characters with a level of recognition confidence for the sequence.

License plate detection and recognition system

An intelligent face recognition system for an installed security system can include a camera and a local or remote video processor including a face recognition engine. The video processor can be configured to receive the image information from the one or more cameras, and generate an alert for communication to a user device based on a recognition event in the environment. In an example, the face recognition engine is configured to apply machine learning to analyze images from the camera and determine whether the images include or correspond to an enrolled face, and the face recognition engine is configured to provide the recognition event based on the determination.

Security system with face recognition

The invention is based, in part, on a system and method designed to be able to easily and automatically scale up to millions of cameras and users. To do this, this discourse teaches use of modern cloud computing technology, including automated service provisioning, automated virtual machine migration services, RESTful API, and various firewall traversing methods to facilitate the scaling process. Moreover, the system and method described herein teaches scalable cloud solutions providing for higher though-put camera provisioning and event recognition. The network may segregate the retrieval server from the storage server, and by doing so, minimizing the load on any one server and improving network efficiency and scalability.

Cloud-based segregated video storage and retrieval for improved network scalability and throughput

A system and method for detecting human presence in or absence from a field-of-view of a camera by analyzing camera data using a processor inside of or adjacent to the camera itself. In an example, the camera can be integrated with or embedded in another edge-based sensor device. In an example, a video signal processing system receives image data from one or more image sensors and uses a local processing circuit to process the image data and determine if a human being is or is not present during a particular time, interval, or sequence of frames. In an example, the human being identification technique can be used in security or surveillance applications such as for home, business, or other monitoring cameras.

Growing community of inventors

San Jose, CA, United States of America

Krishna Khadloya