| CPC G06V 40/103 (2022.01) [B60W 60/0027 (2020.02); G06F 18/295 (2023.01); G06N 3/08 (2013.01); G06N 7/01 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); B60W 2554/4046 (2020.02)] | 20 Claims |
|
1. A method comprising:
receiving a plurality of sequential images comprising a first image depicting a human captured at a first time and a second image depicting the human captured at a second time later than the first time;
inputting at least a portion of the first image into a hybrid model comprising a deep learning model and a probabilistic graphical model, the deep learning model comprising a multi-task model having different branches, each different branch trained to determine a different feature;
receiving, as output from the hybrid model, a plurality of probabilities corresponding to a probability for a given variable feature corresponding to different states of the human;
inputting the plurality of probabilities and the second image into the hybrid model and receiving, as output from the hybrid model, a confidence value that the human will exhibit a behavior; and
outputting the confidence value that the human will exhibit the behavior to a control system.
|