Everseen Limited

Dan Pescaru

Alan O'Herlihy

Cosmin Cernazanu-Glavan

Vasile Gui

Bogdan Ciubotaru

Ciprian Petru David

Joe Allen

Ovidiu Parvu

Gavin Doyle

Dan Alin Crisfalusi

Alexandru Arion

Andrei Pricochi

Mark Ibbotson

Raymond Hegarty

Alan O'herlihy

Cosmin Cernazanu

Arion Alexandru

Cristina Todoran

David Ciprian

Margaret Hartnett

Cosmin Cernăzanu-Glăvan

Cosmin Cernazanu Glavan

Constantin Petru Sacrieru

Gui Vasile

Razvan Cioarga

A method and apparatus for the identification of suspect behaviour in a retail environment, the method comprising: detecting a person in a frame of said stream of video data; extracting a set of activities of the identified person from the stream of video data; assigning a numeric value to each extracted activity in the set of extracted activities, said numeric value representative of a threat level of the activity; accumulating said numeric values to provide a behaviour score; and identifying a behaviour as being suspect when the behaviour score reaches a target threshold value associated with the behaviour.

Method and apparatus for the detection of behaviours in a retail environment

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

A system for process shaping in a retail store environment comprises a video generation and processing component, a data source integration and aggregation component for aggregating and integrate information received from various sources, a process sensing component for generating one or more continuous processes, a process aggregator and weighing component for aggregating the one or more continuous processes into a merged weighted process, a proof of problem and value component for determining one or more process variations, a ripple effect analyser for sending one or more nudging messages to the retail store environment, and a gamified feedback algorithm component for communicating a nudging action corresponding to a nudging message, to one or more entities in the retail store environment.

System and method for process shaping

A POS terminal for a retail environment, comprising a data acquisition device for generating event data from a user scan of a product along a scanning path of the POS terminal, said path comprising an entry point and an exit point; and a processing unit for receiving at least a portion of event data from the data acquisition device and process the event data according to a set of predefined rules, to determine a control parameter for the POS terminal, generate a user alert based on the control parameter and communicate the user alert to the end user; the data acquisition device comprising: a universal product code, U PC, scanner; a plurality of illumination units arranged along the scanning path for illuminating the product; a plurality of sensors for sensing event data including a shape, size and volume of the product and a parameter of a hand of the user as the product is scanned along the scanning path.

POS terminal

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 system for assigning a social attribute class to a human subject in a predefined closed environment includes an image capturing component, a pose detection component configured to perform pose detection and tracking of a human subject in real-time, an action detection component configured to detect an action of the human subject, an activity detection component configured to relate a sequence of actions to detect an activity of the human subject, and a social attribute classification component. The social attribute classification component is configured to determine an average speed (s) of the human subject as a first social attribute, an interaction time of the human subject (T) as a second social attribute, an analysis time (T) as a third social attribute, and automatically assigns a social attribute class to the human subject based on the values of the first, second and third social attributes.

Method and system for determining a human social behavior classification

A system for anomaly monitoring in a pre-defined environment includes one or more image capturing devices configured to capture one or more images of the pre-defined environment in real-time, a process detecting component configured to detect one or more target processes based on captured image data, each target process representing a sequence of user activities in the pre-defined environment, a blockchain unit configured to store the one or more target processes and a reference process in form of a private distributed logbook, wherein each of the target processes and the reference process include a series of process transactions, wherein each process transaction is stored in an encrypted form in the blockchain unit, and an analysis unit communicatively coupled to the blockchain unit, and configured to analyse the private distributed logbook by comparing each target process with the reference process to determine one or more anomalies therein.

Distributed logbook for anomaly monitoring

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 detecting an articulate body pose from an imagery content includes an imaging module for capturing the imagery content, and a processor that is operable to obtain a top-down view of the imagery content, and process the top-down view to detect the articulate body pose using a machine learning algorithm, wherein the articulate body pose includes a plurality of joints. The processing includes creating a part confidence map corresponding to each joint of the articulate body pose, generating a heatmap by projecting the part confidence map on the top-down view of the imagery content, creating a part affinity map corresponding to each body part, generating a vector map by projecting the part affinity map on the top-down view of the imagery content, and generating a body-framework corresponding to the articulate body pose, using the heatmap and the vector map.

System and method to detect articulate body pose

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.

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Timisoara, Romania

Dan Pescaru