CPC G06N 5/022 (2013.01) [G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06T 7/292 (2017.01); G06T 7/70 (2017.01); G06V 10/764 (2022.01); G06V 20/30 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); G06T 2207/10044 (2013.01); G06T 2207/10052 (2013.01); G06T 2207/30261 (2013.01)] | 20 Claims |
1. A computer-implemented method for machine-learned model training, the method comprising:
inputting, into an object association model, training data indicative of a plurality of training objects and a plurality of training object tracks in an environment over a plurality of time intervals;
receiving, from the object association model, an output indicative of a training association of at least one training object at a most recent time interval of the plurality of time intervals and at least one training object track at a plurality of time intervals preceding the most recent time interval;
determining, for the object association model, a similarity score based on a comparison of the training association to a ground truth association, wherein the similarity score is positively correlated with greater accuracy of association by the object association model; and
adjusting at least one parameter of the object association model based on the similarity score.
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