Systems and methods of product recognition through multi-model image processing

1. A system to recognize retail products in a physical retail store, comprising:

a portable user device comprising:

an imaging system configured to capture a series of frames;

at least one tangible memory storing a local product database comprising sets of product imaging data, wherein each set of product imaging data corresponds to one of multiple different retail products; and

a decision control circuit communicatively coupled with the memory and configured to:

process each frame of a set of frames, captured by the imaging system, by at least a first modeling technique relative to a first image attribute and obtain a corresponding first product identification probability that an item, captured within each of the set of frames, is estimated to be a first product;

process each frame of the set of frames by a second modeling technique relative to a second image attribute that is different than the first image attribute, and obtain a corresponding second product identification probability that the item, captured within each of the set of frames, is estimated to be the first product;

collectively evaluate first product identification probabilities and second product identification probabilities of the first product; and

identify, based on the collective evaluation, when one or more of the first product identification probabilities and the second product identification probabilities has a predefined relationship with a collective threshold probability and cause an image of the first product to be displayed in response to identifying that the predefined relationship with the collective threshold probability exists.

2. The system of claim 1 , wherein the decision control circuit is further configured to determine an aggregated first identification probability of the first product as a function of the first product identification probabilities corresponding to the set of frames, and wherein the decision control circuit in determining when one or more of the first product identification probabilities and the second product identification probabilities has the predefined relationship with the collective threshold probability is configured to determine when the aggregated first identification probability has the predefined relationship with the collective threshold probability.

3. The system of claim 2 , wherein the decision control circuit is further configured to determine an aggregated second identification probability of the first product as a function of the second product identification probabilities corresponding to the set of frames, and

wherein the decision control circuit in determining when one or more of the first product identification probabilities and the second product identification probabilities has the predefined relationship with the collective threshold probability is configured to determine when at least one of the aggregated first identification probability and the aggregated second identification probability has the predefined relationship with the collective threshold probability.

4. The system of claim 1 , wherein the second modeling technique comprises a barcode recognition modeling technique and the second image attribute comprises a barcode image attribute, wherein the decision control circuit in obtaining the second product identification probabilities is configured to process each frame of the set of frames by the barcode recognition modeling technique relative to the barcode image attribute, and obtain corresponding barcode product identification probabilities that the item, captured within each of the set of frames, is estimated to be the first product.

5. The system of claim 1 , wherein the first modeling technique comprises optical character recognition (OCR) modeling technique and the decision control circuit in processing each frame of the set of frames according to the first modeling technique is configured to process each frame of the set of frames by the OCR modeling technique relative to text image attributes, and obtain corresponding text product identification probabilities that the item, captured within each of the set of frames, is estimated to be the first product.

6. The system of claim 1 , wherein the decision control circuit in collectively evaluating the first product identification probabilities and the second product identification probabilities is configured to:

apply a first weighting relative to the first product identification probabilities as a function of an expected degree of accuracy relative to the first image attribute to provide at least one weighted first product identification probability;

apply a second weighting relative to the second product identification probabilities as a function of an expected degree of accuracy relative to the second image attribute to provide at least one weighted second product identification probability;

identify when there is a threshold consistency between the at least one weighted first product identification probability and the at least one weighted second product identification probability; and

cause an image of the first product to be displayed in response to identifying that the predefined relationship with the collective threshold probability exists.

7. The system of claim 6 , wherein the decision control circuit is configured to identify a threshold inconsistency between one or more of the first product identification probabilities and one or more of the second product identification probabilities, and apply at least one of the first weighting and the second weighting in response to identifying the threshold inconsistency between the one or more of the first product identification probabilities and the one or more of the second product identification probabilities.

8. The system of claim 6 , wherein the decision control circuit, in applying the second weighting to the second product identification probability, is configured to identify a number of textual words detected in each frame of the set of frames that are present on the image of the first product and multiply the number of textual words by a word multiplier to define the second weighting.

9. The system of claim 1 , wherein the decision control circuit is further configured to:

statistically process the first product identification probabilities and obtain an aggregated first identification probability;

statistically process the second product identification probabilities and obtain an aggregated second identification probability;

identify when there is a threshold inconsistency between the aggregated first identification probability and the aggregated second identification probability; and

apply, in response to determining there is a threshold inconsistency between the aggregated first identification probability and the aggregated second identification probability, a first weighting to the aggregated first identification probability as a function of an expected degree of accuracy relative to the first image attribute to provide a weighted first product identification probability, apply a second weighting to the aggregated second identification probability as a function of an expected degree of accuracy relative to the second image attribute to provide a weighted second product identification probability, and determine a resultant identification probability of the first product based on the weighted first product identification probability and the weighted second product identification probability.

10. The system of claim 1 , wherein the portable user device further comprising a control circuit coupled with the memory and configured to:

add the first product to a virtual cart; and

initiate a checkout of and payment for each product within the virtual shopping cart.

11. The system of claim 10 , wherein the control circuit in initiating the checkout of the virtual cart activates a generation, at a central server, of an order corresponding to the virtual cart and each product included in the virtual cart, obtain a dynamically generated optical machine-readable representation of the order corresponding to the virtual cart, wherein the optical machine-readable representation of the order is configured to be scanned by a scanning system associated with a point of sale system to acquire cost information of the products in the virtual cart.

12. The system of claim 10 , wherein the control circuit in initiating the checkout of the virtual cart activates a generation at a central server of an order corresponding to the virtual cart and each product included in the virtual cart, authorize payment for the products represented in the virtual cart, and receive a confirmation of payment at the portable user device, wherein the confirmation of payment is configured to be displayed on a display of the portable user device to confirm payment prior to a corresponding customer leaving the retail store.

13. A method to recognize retail products in a physical retail store, comprising:

providing a decision control circuit, by the decision control circuit:

receiving video content comprising a series of frames;

processing each frame of a set of frames of the video content by at least a first modeling technique relative to a first image attribute and obtaining a corresponding first product identification probability that an item, captured within each of the set of frames, is estimated to be a first product;

processing each frame of the set of frames by a second modeling technique relative to a second image attribute that is different than the first image attribute and obtaining corresponding a second product identification probabilities that the item, captured within each of the set of frames, is estimated to be the first product;

collectively evaluating first product identification probabilities and second product identification probabilities of the first product;

identifying, based on the collective evaluation, when one or more of the first product identification probabilities and the second product identification probabilities has a predefined relationship with a collective threshold probability; and

causing an image of the first product to be displayed in response to identifying that the predefined relationship with the collective threshold probability exists.

14. The method of claim 13 , further comprising:

determining an aggregated first identification probability of the first product as a function of the first product identification probabilities corresponding to the set of frames; and

wherein the determining when one or more of the first product identification probabilities and the second product identification probabilities has the predefined relationship with the collective threshold probability comprises determining when the aggregated first identification probability has the predefined relationship with the collective threshold probability.

15. The method of claim 14 , further comprising:

determining an aggregated second identification probability of the first product as a function of the second product identification probabilities corresponding to the set of frames; and

wherein the determining when one or more of the first product identification probabilities and the second product identification probabilities has the predefined relationship with the collective threshold probability comprises determining when at least one of the aggregated first identification probability and the aggregated second identification probability has the predefined relationship with the collective threshold probability.

16. The method of claim 13 , wherein the second modeling technique comprises a barcode recognition modeling technique and the second image attribute comprises a barcode image attribute, wherein the obtaining the second product identification probabilities comprises:

processing each frame of the set of frames by the barcode recognition modeling technique relative to the barcode image attribute; and

obtaining corresponding barcode product identification probabilities that the item, captured within each of the set of frames, is estimated to be the first product.

17. The method of claim 13 , wherein the processing each frame of the set of frames by the first modeling technique comprises processing each frame of the set of frames by an optical character recognition (OCR) modeling technique relative to text image attributes; and

obtaining corresponding text product identification probabilities that the item, captured within each of the set of frames, is estimated to be the first product.

18. The method of claim 13 , wherein the collectively evaluating the first product identification probabilities and the second product identification probabilities comprises:

applying a first weighting relative to the first identification probabilities as a function of an expected degree of accuracy relative to the first image attribute to provide at least one weighted first product identification probability;

applying a second weighting relative to the second identification probabilities as a function of an expected degree of accuracy relative to the second image attribute to provide at least one weighted second product identification probability;

identify when there is a threshold consistency between the at least one weighted first product identification probability and the at least one weighted second product identification probability; and

causing an image of the first product to be displayed in response to identifying that the predefined relationship with the collective threshold probability exists.

19. The method of claim 18 , further comprising:

identifying a threshold inconsistency between one or more of the first product identification probabilities and one or more of the second product identification probabilities; and

applying at least one of the first weighting and the second weighting in response to identifying the threshold inconsistency between the one or more of the first product identification probabilities and the one or more of the second product identification probabilities.

20. The method of claim 18 , wherein the applying the second weighting to the second product identification probability comprises identifying a number of textual words detected in each frame of the set of frames that are present on the image of the first product; and

multiplying the number of textual words by a word multiplier to define the second weighting.

21. The method of claim 13 , further comprising:

statistically processing the first product identification probabilities and obtaining an aggregated first identification probability;

statistically processing the second product identification probabilities and obtaining an aggregated second identification probability;

identifying when there is a threshold inconsistency between the aggregated first identification probability and the aggregated second identification probability;

applying, in response to determining there is a threshold inconsistency between the aggregated first identification probability and the aggregated second identification probability, a first weighting to the aggregated first identification probability as a function of an expected degree of accuracy relative to the first image attribute to provide a weighted first product identification probability, and applying a second weighting to the aggregated second identification probability as a function of an expected degree of accuracy relative to the second image attribute to provide a weighted second product identification probability; and

determining a resultant identification probability of the first product based on the weighted first product identification probability and the weighted second product identification probability.

22. The method of claim 13 , further comprising:

add the first product to a virtual cart; and

initiate a checkout of and payment for each product within the virtual shopping cart.