Machine learning solutions for matching of vehicle exit to unmatched entry events
An edge device generates an exit event for a vehicle exiting a parking facility. The edge device determines whether the exit event matches with an entry event. Responsive to determining that the exit event does not match to an entry event, the edge device inputs images of the vehicle into a supervised machine learning model and receives, as output from the model, an exit feature vector. The edge device retrieves entry feature vectors corresponding to hanging entry events. A hanging entry event is an entry event for a vehicle with an unknown vehicle identifier. Edge device inputs the exit feature vector and the entry feature vectors into an unsupervised machine learning model and receives, as output from the model, matching scores for each entry feature vector. Edge device matches the exit event to one of the hanging entry events based on the matching scores.
1. A method comprising:
generating an exit event that represents a vehicle exiting a parking facility;
determining whether the exit event matches an entry event that also represents the vehicle; and
responsive to determining that the exit event does not match the entry event:
inputting a depiction of the vehicle into a supervised machine learning model;
receiving, as output from the supervised machine learning model, an exit feature vector comprising a plurality of embeddings that are each derived from a dimension of the depiction of the vehicle;
retrieving a set of entry feature vectors, wherein an entry feature vector is generated using a given vehicle represented by a given entry event of a group of hanging entry events, each of the hanging entry events associated with a vehicle that is not paired with a corresponding exit event;
inputting the exit feature vector and the set of entry feature vectors into an unsupervised machine learning model;
receiving, as output from the unsupervised machine learning model, a plurality of matching scores comprising a matching score for each entry feature vector in the set of entry feature vectors; and
matching the exit event to one or more of the hanging entry events based on the plurality of matching scores.
2. The method of claim 1 , wherein the exit event comprises one or more images of the vehicle exiting the parking facility.
3. The method of claim 2 , wherein inputting the depiction of the vehicle into the supervised machine learning model comprises:
isolating, from the one or more images, a first image portion containing the vehicle; and
excluding, from the one or more images, a second image portion that does not contain the vehicle.
4. The method of claim 1 , further comprising responsive to determining that the exit event matches an entry event that also represents the vehicle, matching the exit event to the entry event.
5. The method of claim 1 , wherein determining that the exit event matches an entry event that also represents the vehicle comprises comparing a vehicle identifier of the vehicle to a set of vehicle identifiers.
6. The method of claim 5 , wherein comparing the vehicle identifier of the vehicle to a set of vehicle identifiers comprises:
inputting the vehicle identifier corresponding to the vehicle and the set of vehicle identifiers into a machine learning model; and
receiving, as output from the machine learning model, a plurality of matching scores comprising a matching score for each vehicle identifier in the set of vehicle identifiers.
7. The method of claim 5 , wherein the vehicle identifier is comprised of vertical and horizontal characters.
8. The method of claim 1 , further comprising:
generating an entry event that represents a vehicle entering the parking facility;
determining whether the vehicle is in a candidate set of known vehicles; and
responsive to determining that the vehicle is not in the candidate set of known vehicles, retrieving the entry feature vector corresponding to the vehicle.
9. The method of claim 8 , wherein the candidate set of known vehicles comprises vehicles with profiles stored in a profile database.
10. The method of claim 1 , further comprising providing, for display, a user interface comprising one or more matches between the exit event and the one or more hanging entry events.
11. The method of claim 1 , wherein matching the exit event to one or more of the hanging entry events comprises automatically matching the exit event to the hanging entry event with a highest matching score.
12. The method of claim 1 , wherein the matching score represents how well the entry feature vector matches the exit feature vector.
13. A non-transitory computer-readable medium comprising memory with instructions encoded thereon, the instructions comprising instructions to:
generate an exit event that represents a vehicle exiting a parking facility;
determine whether the exit event matches an entry event that also represents the vehicle; and
responsive to determining that the exit event does not match the entry event:
input a depiction of the vehicle into a supervised machine learning model;
receive, as output from the supervised machine learning model, an exit feature vector comprising a plurality of embeddings that are each derived from a dimension of the depiction of the vehicle;
retrieve a set of entry feature vectors, wherein an entry feature vector is generated using a given vehicle represented by a given entry event of a group of hanging entry events, each of the hanging entry events associated with a vehicle that is not paired with a corresponding exit event;
input the exit feature vector and the set of entry feature vectors into an unsupervised machine learning model;
receive, as output from the unsupervised machine learning model, a plurality of matching scores comprising a matching score for each entry feature vector in the set of entry feature vectors; and
match the exit event to one or more of the hanging entry events based on the plurality of matching scores.
14. The non-transitory computer-readable medium of claim 13 , wherein the exit event comprises one or more images of the vehicle exiting the parking facility.
15. The non-transitory computer-readable medium of claim 14 , wherein the instructions to input the depiction of the vehicle into the supervised machine learning model comprise instructions to:
isolate, from the one or more images, a first image portion containing the vehicle; and
exclude, from the one or more images, a second image portion that does not contain the vehicle.
16. The non-transitory computer-readable medium of claim 13 , further comprising instructions to, responsive to determining that the exit event matches an entry event that also represents the vehicle, match the exit event to the entry event.
17. The non-transitory computer-readable medium of claim 13 , wherein the instructions to determine that the exit event matches an entry event that also represents the vehicle comprise instructions to compare a vehicle identifier of the vehicle to a set of vehicle identifiers.
18. The non-transitory computer-readable medium of claim 17 , wherein the instructions to compare the vehicle identifier of the vehicle to a set of vehicle identifiers comprise instructions to:
input the vehicle identifier corresponding to the vehicle and the set of vehicle identifiers into a machine learning model; and
receive, as output from the machine learning model, a plurality of matching scores comprising a matching score for each vehicle identifier in the set of vehicle identifiers.
19. The non-transitory computer-readable medium of claim 17 , wherein the vehicle identifier is comprised of vertical and horizontal characters.
20. A system comprising:
memory with instructions encoded thereon; and
one or more processors that, when executing the instructions, are caused to perform operations comprising:
generating an exit event that represents a vehicle exiting a parking facility;
determining whether the exit event matches an entry event that also represents the vehicle; and
responsive to determining that the exit event does not match the entry event:
inputting a depiction of the vehicle into a supervised machine learning model;
receiving, as output from the supervised machine learning model, an exit feature vector comprising a plurality of embeddings that are each derived from a dimension of the depiction of the vehicle;
retrieving a set of entry feature vectors, wherein an entry feature vector is generated using a given vehicle represented by a given entry event of a group of hanging entry events, each of the hanging entry events associated with a vehicle that is not paired with a corresponding exit event;
inputting the exit feature vector and the set of entry feature vectors into an unsupervised machine learning model;
receiving, as output from the unsupervised machine learning model, a plurality of matching scores comprising a matching score for each entry feature vector in the set of entry feature vectors; and
matching the exit event to one or more of the hanging entry events based on the plurality of matching scores.