Machine learning and computer vision solutions to seamless vehicle identification and environmental tracking therefor

A non-transitory computer-readable medium with memory encoded thereon comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the instructions comprising instructions to:

• capture a series of images over time in featuring a vehicle approaching a parking apparatus;
• determine, from at least a subset of images of the series of images featuring the vehicle:
• a plurality of parameters that describe attributes of the vehicle by inputting the subset of images into a first machine learning model;
• and
• a vehicle identifier of the vehicle by inputting images of the subset featuring a depiction of a license plate of the vehicle into a second machine learning model;
• verify a status of the vehicle based on a concordance of the plurality of parameters and the vehicle identifier;
• and
• responsive to verifying the status, instruct the parking apparatus to perform an operation.

A system comprising:

• memory with instructions encoded thereon;
• and
• one or more processors that, when executing the instructions, are caused to perform operations, the operations comprising:
• capturing a series of images over time in featuring a vehicle approaching a parking apparatus;
• determining, from at least a subset of images of the series of images featuring the vehicle:
• a plurality of parameters that describe attributes of the vehicle by inputting the subset of images into a first machine learning model;
• and
• a vehicle identifier of the vehicle by inputting images of the subset featuring a depiction of a license plate of the vehicle into a second machine learning model;
• verifying a status of the vehicle based on a concordance of the plurality of parameters and the vehicle identifier;
• and
• responsive to verifying the status, instructing the parking apparatus to perform an operation.