MACHINE LEARNING AND COMPUTER VISION SOLUTIONS TO SEAMLESS VEHICLE IDENTIFICATION AND ENVIRONMENTAL TRACKING THEREFOR

A device captures a series of images over time in association with a gate, each image having a timestamp. The device determines, for a vehicle approaching the entry side, from a subset of images of the series of images featuring the vehicle, a first data set comprising 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. The device stores the data set in association with one or more timestamps with the subset of images, determines a second data set for a second vehicle approaching the exit side, and responsive to determining that the first data set and the second data set match, instructs the gate to move.