Honeywell International Inc.

Honeywell G.m.b.h.

Ademco Inc.

Image Sensing Systems, Inc.

Saad J Bedros

Kwong Wing Au

Sharath Venkatesha

Gurumurthy Swaminathan

Ben Miller

Jana Trojanova

Vassilios Morellas

Kartik B Ariyur

Pedro Davalos

Keith L Curtner

Steve D Huseth

Tom Plocher

Rida M Hamza

Jan Jelinek

Alan L Haggerty

Michael Janssen

KwongWing Au

Ezekiel Kruglick

Soumitri N Kolavennu

Isaac D Cohen

Cleopatra Cabuz

Michael Ray Elgersma

Henry Chen

Mohammed Ibrahim Mohideen

Robert Charles Becker

Himanshu Khurana

Alan Cornett

Hari Thiruvengada

Pradeep Shetty

Rand P Whillock

Paul Derby

Vinod Pathangay

Mahesh Kumar Gellaboina

Thomas L Phinney

Dennis William Strelow

Darryl Busch

Gary Edward Determan

Vijendran Gopalan Venkoparao

Vincent Clarence Jacobson

Adishesha Cs

George A Kilgore

Roland Miezianko

Matthew E L Jungwirth

Michael J Jaques

Terry Ahrens

James E Ingalls

Yadhunandan Ullam Subbaraya

Tom Markham

Thirumaran Ekambaram

Michal Juza

Rudra N Hota

James Cikanek

Levi Remily

Zachary Pearson

Ullam Subbaraya Yadhunandan

Randall Johnson

Nasir Mohammed

Michael John Flanagan

Mirela Onorica Popa

George F Marschall

Seth Anderson

Aaron Becker

Mahesh Kumar Gellabonia

Systems and methods for training and using machine learning models to classify vehicles from highway radar systems are provided. The training systems may use auxiliary radar processing to separate events by lane, length, and/or speed, and then use separate event data groups pooled from similar or proximate lanes, lengths, and/or speeds to train multiple models. At estimation time, incoming events may be grouped using similar groupings as those used during training to select which model to use. An incoming event may be applied to the neural network operations of the selected model to generate an estimate. Generating an estimate may involve successive applications of multiple linear convolutions and other steps along varying or alternating dimensions of the in-process data.

Machine learning based highway radar vehicle classification across multiple lanes and speeds

A context-aware smart home energy management (CASHEM) system and method is disclosed. CASHEM dynamically schedules household energy use to reduce energy consumption by identifying contextual information within said household, selecting a comfort of service preference, wherein said comfort of service preference is based on different said contextual information, and extracting an appliance use schedule for maximum energy savings based on said contextual information in light of said comfort of service preferences, by executing a program instruction in a data processing apparatus. CASHEM correlates said contextual information with energy consumption levels to dynamically schedule said appliance based on an energy-saving condition and a user's comfort. Comfort of service preferences are gathered by CASHEM by monitoring occupant activity levels and use of said appliance. CASHEM can also recommend potential energy savings for a user to modify comfort of service preferences.

Context-aware smart home energy manager

Adaptive optical zoom systems and methods are described herein. One example of a method for adaptive optical zoom includes receiving an image at a focal plane array through a number of variable focal length elements, determining a quality of the image, and altering an amperage of the number of variable focal length elements to increase the quality of the image.

Adaptive optical zoom systems and methods

A method comprises receiving one or more images of a person from one or more image capture devices in response to an input, identifying one or more items of personal protective equipment in the one or more images, determining the positioning of the one or more items of personal protective equipment relative to the person in the one or more images, and verifying compliance with personal protective equipment standards based on the one or more identified items of personal protective equipment and the positioning of the one or more items of personal protective equipment.

Image recognition for personal protective equipment compliance enforcement in work areas

A method and system for automatic face recognition. A primary and a plurality of secondary video cameras can be provided to monitor a detection area. The primary video camera can detect people present in the detection zone. Data can be then transmitted to a prioritizor module that produces a prioritized list of detected people. The plurality of secondary video cameras then captures a high-resolution image of the faces of the people present in the detection area according to the prioritized list provided by the prioritizor module. The high-resolution images can be then provided to a face recognition module, which is used to identify the people present in the detection area.

Distant face recognition system

A method includes receiving an image of a face to match with images of known faces, extracting blocks multiple blocks from the received image, calculating local binary pattern histograms for each block, generating matching scores for each block against block of the images of known faces, determining a top number, N, of matching scores less than the number of blocks, and matching the received image to an image of a known face as a function of the top number of matching scores.

Face matching for mobile devices

Systems, methods, and computer-readable and executable instructions are provided for detecting a use of personal protective equipment (PPE). Detecting a use of PPE can include collecting contact data from a number of touch sensitive sensors attached to the PPE. Furthermore, detecting a use of PPE can include determining if the PPE is in proper use based on the collected contact data.

Personal protection equipment verification

A system receives an iris image and segments the iris region. The segmented iris region is mapped to a unit disk and partitioned into local iris regions (or sectors) as a function of the radius and angle The system calculates localized Zernike moments for a plurality of regions of the unit disk. The localized Zernike moment includes a projection of the local iris region into a space of Zernike polynomial orthogonal basis functions. The system generates an iris feature set from the localized Zernike moments for each partitioned region, excluding the regions which are comprised by occlusion. The iris features are weighted based on the conditions of blur, gaze and occlusion of the iris region. A probe iris image is then matched to a plurality of iris images in a database based on the distance of its feature set to the corresponding plurality of iris feature sets.

Iris recognition using localized Zernike moments

A condition based method that selects an appropriate approach among various iris and ocular image recognition algorithms for matching periocular images of a probe and target as a function of quality of images to obtain robust matching even under non-ideal acquisition scenarios.

Ocular and iris processing system and method

The present disclosure provides methods, machine readable media, and systems for object alignment from a 2-dimensional (2-D) image of the object. One or more embodiments include defining a 2-D shape in the 2-D image and a 3-dimensional (3-D) shape in a 3-D model of the object, mapping a number of corresponding points on the 2-D and 3-D shapes, defining the 2-D and 3-D shapes with a number of triangles, wherein a number of vertices of the number of triangles correspond to the number of points, subdividing the number of triangles defining the 2-D and 3-D shapes into a plurality of subdivided triangles that include a plurality of new vertices, and reconstructuring a 3-D image from the 2-D image by assigning a number of z-coordinates from the plurality of subdivided triangles of the 3-D shape to the plurality of subdivided triangles of the 2-D shape to create a 3-D reconstructured shape.

Object alignment from a 2-dimensional image

A method and system include receiving an image to process for an image recognition system, determining a quality of the received image, and creating a point distribution model for an active shape model, wherein the point distribution model has a number of points defining an outline of the image, the number of points being determined as a function of the quality of the image. A further method includes selecting a target local appearance model for fitting a point as a function of the determined quality of the received image to determine the location of the point. Yet a further method includes matching the probe image to a plurality of target images using a quality driven cascade classifier.

Quality driven image processing for ocular recognition system

Methods, machine-readable media, and devices for face image prioritization based on face quality analysis are described herein. For example, one or more embodiments include detecting a facial image in an image that has been acquired by a camera that monitors a scene, passing the facial image through a number of quality analysis filters that include a number of quality analysis factors, wherein processing complexity associated with the number of quality analysis factors increases consecutively, and submitting the facial image to a facial recognition program upon a determination that the facial image has passed the number of quality analysis filters.

Face image prioritization based on face quality analysis

A security system is configured to display a representation of an area on a display unit, receive input from a plurality of different types of sensors that are positioned within the area, display a plurality of icons on the display unit, wherein the plurality of icons represents the locations of the plurality sensors in the area, and display in a panel on the display unit output from the plurality of sensors.

User interfaces for enabling information infusion to improve situation awareness

A system and method for automatically determining the camera field of view in a camera network. The system has a plurality of spatially separated cameras and direction sensors, carried on respective cameras, configured to measure the angle directions of the field of views of the cameras. Elevation sensors are operably coupled to respective cameras to measure the elevation angles of thereof. A controller is configured to process direction and elevation measurement signals transmitted from the cameras to automatically determine the cameras' fields of views. One or more cameras having a field of view containing or nearby an event of interest can be selected from the determined field of views and indicated to a user via a graphical user interface. Selected cameras which are rotatably mounted can be rotated if need be to automatically bring the event of interest into the field of views of the selected cameras.

Method and system for automatically determining the camera field of view in a camera network

A system and method track a face moving through multiple frames of a video sequence. A predicted position of a face in a video frame is obtained. A search is performed to determine appearance model similarity values of a detected face. Motion and appearance probabilities of the detected face are obtained, and the detected face in the video frame is associated with a track as a function of a joint probabilistic data association filter.

Multiple view face tracking

Growing community of inventors

West St. Paul, MN, United States of America

Saad J Bedros