Nandor Gyorgy Thoma - Plano TX Trong Duc Nguyen - Austin TX
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
H03H 1116
US Classification:
327239
Abstract:
A signal delay device is provided which enhances noise immunity by using a differential circuit, but also maintains the phase of the input clock signals. This device will also correct the phase of clock signals which are input to the delay device in an out of phase condition. The present invention is a delay circuit that includes functionally connecting each of the output signals with each of the input signals. Thus, the output signals are dependent on the same input and the steady state condition is the point where the leading edge of a first output signal intersects the trailing edge of a second output signal at the point which corresponds to one half of the pulse height of both signals. Since the signals are complements of one another, they will cross at 50% of their pulse height when they are "in phase". Thus, the present invention will maintain "in phase" input signals and seek an "in phase" condition for signals that are input to the delay circuit which are "out of phase".
System And Method For Populating A Virtual Shopping Cart Based On Video Of A Customer's Shopping Session At A Physical Store
- Irving TX, US Shahmeer Ali Mirza - Celina TX, US Joshua E. Berry - Waco TX, US Trong Nghia Nguyen - Dallas TX, US Ravi Kumar Kurva - Irving TX, US Sarath Vakacharla - Irving TX, US Ranganathan Mohan - Frisco TX, US Maninder Singh Suri - Irving TX, US Jonathan Christopher Hodge - Frisco TX, US
International Classification:
G06Q 30/06 G09G 5/14
Abstract:
An apparatus includes a display, interface, and processor. The interface receives video from a camera located in a physical store and directed at a first physical rack. The camera captures video of the rack during a shopping session. The processor displays a first virtual rack that emulates the first physical rack and includes first and second virtual shelves. The virtual shelves include virtual items, which include graphical representations of physical items located on the physical rack. The processor displays the rack video, which depicts an event including the person interacting with the first physical rack. The processor also displays a virtual shopping cart. The processor receives information associated with the event, identifying the first virtual item. The rack video depicts that the person selected the first physical item while interacting with the first physical rack. The processor then stores the first virtual item in the virtual shopping cart.
- Irving TX, US Shahmeer Ali Mirza - Celina TX, US Sarath Vakacharla - Irving TX, US Trong Nghia Nguyen - Dallas TX, US Crystal Maung - Dallas TX, US Deepanjan Paul - Plano TX, US
International Classification:
G06K 9/00 G06T 7/292 G06T 7/246 G06T 7/215
Abstract:
An object tracking system includes a sensor and a tracking system. The sensor is configured to capture a frame of at least a portion of a physical space within a global plane for a space. The tracking system is configured to receive the frame, to detect an object within a zone of the frame, and to determine a pixel location for the object. The tracking system is further configured to identify a zone of the physical structure based on the pixel location, to identify an item based on the identified zone.
Determining Candidate Object Identities During Image Tracking
- Irving TX, US Sailesh Bharathwaaj Krishnamurthy - Irving TX, US Trong Nghia Nguyen - Dallas TX, US
International Classification:
G06K 9/00 G06T 7/292 H04N 7/18 G06T 7/246
Abstract:
A system includes sensors and a tracking subsystem. The subsystem receives frames of top-view images generated by the sensors. The subsystem tracks a first and second object, based on received frames. The subsystem detects that the first object is within a threshold distance of the second object. In response, the subsystem determines a probability that the first object switched identifiers with the second object and updates candidate lists accordingly for the first and second objects. The updated first candidate list includes a probability that the first object is associated with a first identifier and a probability that the first object is associated with a second identifier.
Feedback And Training For A Machine Learning Algorithm Configured To Determine Customer Purchases During A Shopping Session At A Physical Store
- Irving TX, US Sailesh Bharathwaaj Krishnamurthy - Irving TX, US Trong Nghia Nguyen - Dallas TX, US Sarath Vakacharla - Irving TX, US
International Classification:
G06K 9/00 G06Q 30/06 G06N 20/00
Abstract:
An apparatus includes a processor. The processor receives an algorithmic shopping cart that includes a first set of items determined by an algorithm to have been selected by a person during a shopping session in a physical store, based on a set of inputs received from sensors located within the physical store. The processor also receives a virtual shopping cart that includes a second set of items. Video of the shopping session was captured by a set of cameras located in the physical store and depicts the person selecting the second set of items. The processor compares the algorithmic cart to the virtual cart and determines that a discrepancy exists between the algorithmic cart and the virtual cart. The processor determines a subset of the set of inputs associated with the discrepancy and attaches metadata explaining the discrepancy to the subset. The processor uses the subset to train the algorithm.
- Irving TX, US Shahmeer Ali Mirza - Celina TX, US Sarath Vakacharla - Irving TX, US Trong Nghia Nguyen - Dallas TX, US Crystal Maung - Dallas TX, US Deepanjan Paul - Plano TX, US
International Classification:
G06K 9/00 G06T 7/292 G06T 7/246 G06T 7/215
Abstract:
An object tracking system includes a sensor and a tracking system. The sensor is configured to capture a frame of at least a portion of a rack within a global plane for a space. The tracking system is configured to receive the frame, to detect an object within a zone of the frame, and to determine a pixel location for the object. The tracking system is further configured to identify a zone and a shelf of the rack based on the pixel location, to identify an item based on the identified zone and the identified shelf of the rack, and to add the identified item to a digital cart associated with a person.
Determining Candidate Object Identities During Image Tracking
- Irving TX, US Sailesh Bharathwaaj Krishnamurthy - Irving TX, US Trong Nghia Nguyen - Dallas TX, US
International Classification:
G06K 9/00 H04N 7/18 G06T 7/292 G06T 7/246
Abstract:
A system includes sensors and a tracking subsystem. The subsystem receives frames of top-view images generated by the sensors. The subsystem tracks a first, second, and third object, based on received frames. The subsystem detects that the first object is within a threshold distance of the second object. In response, the subsystem determines a probability that the first object switched identifiers with the second object and updates candidate lists accordingly for the first and second objects. The updated first candidate list includes a probability that the first object is associated with a first identifier and a probability that the first object is associated with a second identifier. The updated second candidate list includes a probability that the second object is associated with the first identifier and a probability that the second object is associated with the second identifier.
Tracking Positions Using A Scalable Position Tracking System
- Irving TX, US Sarath Vakacharla - Irving TX, US Trong Nghia Nguyen - Dallas TX, US Shahmeer Ali Miza - Celina TX, US Madan Mohan Chinnam - Irving TX, US Caleb Austin Boulio - Lewisville TX, US
International Classification:
G06K 9/00 H04N 5/376 G01S 17/06 H04N 5/247
Abstract:
A scalable tracking system processes video of a space to track the positions of people within a space. The tracking system determines local coordinates for the people within frames of the video and then assigns these coordinates to time windows based on when the frames were received. The tracking system then combines or clusters certain local coordinates that have been assigned to the same time window to determine a combined coordinate for a person during that time window.
BAE Systems Controls, Inc. (Formerly Boeing Commercial Electronics) Irving, TX Apr 2002 to May 2012 Senior Systems/Test Design EngineerBAE Systems Controls, Inc. (Formerly Boeing Commercial Electronics) Irving, TX 1998 to 2002 Test Engineering LeadBAE Systems Controls, Inc. (FormBoeing Commercial Electronics) Irving, TX 1989 to 1998 Test Engineer
Education:
DeVry Institute of Technology Irving, TX 1989 Bachelor of Science in Science Electronics Engineering Technology
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