SYSTEMS AND METHODS FOR MACHINE VISION BASED OBJECT RECOGNITION
The present disclosure is related to object recognition and tracking using multi-camera driven machine vision. In one aspect, a method includes capturing, via a multi-camera system, a plurality of images of a user, each of the plurality of images representing the user from a unique angle; identifying, using the plurality of images, the user; detecting, throughout a facility, an item selected by the user; creating a visual model of the item to track movement of the item throughout the facility; determining, using the visual model, whether the item is selected for purchase; and detecting that the user is leaving the facility; and processing a transaction for the item when the item is selected for purchase and when the user has left the facility.
1 . (canceled)
2 . A computer-implemented method comprising:
receiving one or more images captured by a first set of cameras located within a facility;
applying one or more image-recognition algorithms to the one or more images to determine a presence of a user within the facility;
detecting a selection of an item by the user, wherein the item is located at a first location of the facility;
generating a digital representation of the selected item;
receiving additional images indicating that the digital representation is at a second location of the facility, wherein the additional images are captured by a second set of cameras located in outer perimeters of the facility;
analyzing the additional images to determine that the selected item is approaching an exit of the facility; and
processing a transaction for the selected item after determining that that the selected item is approaching the exit of the facility.
3 . The computer-implemented method of claim 2 , wherein the one or more image-recognition algorithms includes a convolutional neural network.
4 . The computer-implemented method of claim 2 , wherein the second location of the digital representation is determined by transposing the digital representation into a two-dimensional model of the selected item.
5 . The computer-implemented method of claim 2 , wherein determining that selected item is approaching the exit of the facility includes:
determining whether a coordinate of the second location is within a distance threshold associated with an entrance of the facility.
6 . The computer-implemented method of claim 2 , wherein processing the transaction includes:
accessing a profile associated with the user; and
processing the transaction using payment data stored in the profile.
7 . The computer-implemented method of claim 2 , wherein detecting the selection of the item includes generating training data based on the selected item, and wherein the training data is used to train a machine-learning model configured to classify one or more items located within the facility.
8 . The computer-implemented method of claim 2 , wherein detecting the selection of the item includes:
accessing a profile associated with the user; and
updating the profile to identify the selected item.
9 . A system comprising:
one or more processors; and
memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to perform operations comprising:
receiving one or more images captured by a first set of cameras located within a facility;
applying one or more image-recognition algorithms to the one or more images to determine a presence of a user within the facility;
detecting a selection of an item by the user, wherein the item is located at a first location of the facility;
generating a digital representation of the selected item;
receiving additional images indicating that the digital representation is at a second location of the facility, wherein the additional images are captured by a second set of cameras located in outer perimeters of the facility;
analyzing the additional images to determine that the selected item is approaching an exit of the facility; and
processing a transaction for the selected item after determining that that the selected item is approaching the exit of the facility.
10 . The system of claim 9 , wherein the one or more image-recognition algorithms includes a convolutional neural network.
11 . The system of claim 9 , wherein the second location of the digital representation is determined by transposing the digital representation into a two-dimensional model of the selected item.
12 . The system of claim 9 , wherein determining that selected item is approaching the exit of the facility includes:
determining whether a coordinate of the second location is within a distance threshold associated with an entrance of the facility.
13 . The system of claim 9 , wherein processing the transaction includes:
accessing a profile associated with the user; and
processing the transaction using payment data stored in the profile.
14 . The system of claim 9 , wherein detecting the selection of the item includes generating training data based on the selected item, and wherein the training data is used to train a machine-learning model configured to classify one or more items located within the facility.
15 . The system of claim 9 , wherein detecting the selection of the item includes:
accessing a profile associated with the user; and
updating the profile to identify the selected item.
16 . A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to perform operations comprising:
receiving one or more images captured by a first set of cameras located within a facility;
applying one or more image-recognition algorithms to the one or more images to determine a presence of a user within the facility;
detecting a selection of an item by the user, wherein the item is located at a first location of the facility;
generating a digital representation of the selected item;
receiving additional images indicating that the digital representation is at a second location of the facility, wherein the additional images are captured by a second set of cameras located in outer perimeters of the facility;
analyzing the additional images to determine that the selected item is approaching an exit of the facility; and
processing a transaction for the selected item after determining that that the selected item is approaching the exit of the facility.
17 . The non-transitory, computer-readable storage medium of claim 16 , wherein the one or more image-recognition algorithms includes a convolutional neural network.
18 . The non-transitory, computer-readable storage medium of claim 16 , wherein the second location of the digital representation is determined by transposing the digital representation into a two-dimensional model of the selected item.
19 . The non-transitory, computer-readable storage medium of claim 16 , wherein determining that selected item is approaching the exit of the facility includes:
determining whether a coordinate of the second location is within a distance threshold associated with an entrance of the facility.
20 . The non-transitory, computer-readable storage medium of claim 16 , wherein processing the transaction includes:
accessing a profile associated with the user; and
processing the transaction using payment data stored in the profile.
21 . The non-transitory, computer-readable storage medium of claim 16 , wherein detecting the selection of the item includes generating training data based on the selected item, and wherein the training data is used to train a machine-learning model configured to classify one or more items located within the facility.
22 . The non-transitory, computer-readable storage medium of claim 16 , wherein detecting the selection of the item includes:
accessing a profile associated with the user; and
updating the profile to identify the selected item.