Everseen Limited

Cosmin Cernazanu-Glavan

Dan Pescaru

Vasile Gui

Alan O'Herlihy

Ciprian Petru David

Andrei Pricochi

Bogdan Ciubotaru

Joe Allen

Mark Ibbotson

Raymond Hegarty

Gavin Doyle

Margaret Hartnett

David Ciprian

A system for detecting a scan irregularity in scanning process during check-out at a retail store, includes an image receiving module for receiving a video stream of a scanning zone, an image processing module for detecting visual scan intervals in image frames of the video stream, and a decision module. The decision module is configured to process each detected visual scan interval, wherein a processed visual scan interval includes a valid scan action, wherein the valid scan action is a user action performed for scanning an item. The decision module is further configured to detect a scan irregularity in the check-out process, wherein the scan irregularity occurs when an item identified for scanning in a processed visual scan interval is absent in a list of scanned items generated by the scanner during corresponding interval, and provide an alert regarding the scan irregularity at a user computing device.

System and method for detecting scan irregularities at self-checkout terminals

An enhanced service system comprises a pair of first wires between a first position proximal to an entry point of a drive through facility and a second position proximal to a receiving window in the drive through facility, and an aerial host unit coupled to the pair of first wires using a pair of first and second host wires. The aerial host unit is longitudinally movable between the first position and the second position and includes an aerial host member moveably supported by the first and second host wires to move laterally between the pair of first wires to approach an incoming customer vehicle, facilitate the customer to place an order, receive payment from the customer, and direct the customer to the receiving window where the aerial host member retrieves the items corresponding to the placed order from the receiving window and transfers the retrieved items to the customer.

Enhanced service system for a drive through facility

A virtual management system comprises video cameras, and various other sensors that acquire event data indicative relating to the processing of stock. This data is passed to a local data collection device that aggregates the event data and passes it via a network to a number of remote data processing modules. The event data is allocated to each of the data processing modules based upon their assigned tasks by a virtual manager agent. A data processing module receives the aggregated event data from the local data collection device via a network and processes the event data according to a set of pre-defined rules. The data processing module generates an alert in response to the processing of the event data indicating that a predefined event has occurred, and transmits the alert to a remote device associated with an employee.

Virtual management system data processing unit and method with rules and alerts

A system for recognizing human activity from a video stream includes a classifier for classifying an image frame of the video steam in one or more classes and generating a class probability vector for the image frame based on the classification. The system further includes a data filtering and binarization module for filtering and binarizing each probability value of the class probability vector based on a pre-defined probability threshold value. The system furthermore includes a compressed word composition module for determining one or more transitions of one or more classes in consecutive image frames of the video stream and generating a sequence of compressed words based on the determined one or more transitions. The system furthermore includes a sequence dependent classifier for extracting one or more user actions by analyzing the sequence of compressed words to and recognizing human activity therefrom.

Quantized transition change detection for activity recognition

A method for segmenting motion data and inferring layering order in a video includes segmenting a first image frame of the video using intra-frame mode estimation, segmenting further image frames of the video using inter-frame mode matching, generating a label change map for a current segmented image frame by generating a binary map of the current segmented image frame, and then marking one or more pixels whose labels have changed with respect to a previous segmented image frame, detecting one or more non-still image segments of the current segmented image frame, detecting one or more moving image segments in the one or more non-still image segments, estimating motion of the one or more moving image segments, representing each moving segment as a vector of seven parameters, and clustering one or more similar moving segment vectors based on corresponding one or more parameters.

Growing community of inventors

Timisoara Timis, Romania

Cosmin Cernazanu-Glavan