Pedestrian activity recognition is embodied in methods, systems, non-transitory computer-readable, and vehicles. Records of the same pedestrian activity from two or more independently trained image capture devices are trained by training a Siamese neural network to identify a plurality of pedestrian activities. Continuous data collection from additional image capture devices is employed to deploy a Siamese neural network to create clustered datasets of similar activities in an unsupervised manner. A spatial-temporal intent prediction model that can be deployed to identify and predict pedestrian activity is then trained. Based on the likelihood of a particular pedestrian activity being occurring or currently being performed, automatic vehicle maneuvers can be performed to navigate a condition.
Method for pedestrian activity recognition using limited data and meta-learning