Abstract: Methods are provided for automatically analyzing and understanding activities and interactions. One method comprises receiving at least location information for one or more individual objects in a scene at a given time; applying at least one machine learning or artificial intelligence technique to automatically learn an informative representation of location trajectory data for each object; and identifying and analyzing individual and group activities in the scene based on the trajectory data.

Abstract: Effectively training machine learning systems with incomplete/partial labels is a practical, technical problem that solutions described herein attempt to overcome. In particular, an approach to modify loss functions on a proportionality basis is noted in some embodiments. In other embodiments, a graph neural network is provided to help identify correlations/causations as between categories. In another set of embodiments, a prediction approach is described to, based on originally provided labels, predict labels for unlabelled training samples such that the proportion of labelled labels relative to all labels is increased.

Abstract: A variational auto-encoder model is trained to generate probabilities of action categories and probabilities of inter-arrival times of next action from a sequence of past actions by generating a concatenated representation of each action and associated time, encoding the concatenated representations, determining a conditional prior distribution for a next action, determining a conditional posterior distribution for the current action, sampling a latent variable from the conditional prior distribution, generating a probability distribution over a current action category, and generating a probability distribution over inter-arrival times for the current action category.

Abstract: Methods are provided for automatically analyzing and understanding activities and interactions. One method comprises receiving at least location information for one or more individual objects in a scene at a given time; applying at least one machine learning or artificial intelligence technique to automatically learn an informative representation of location trajectory data for each object; and identifying and analyzing individual and group activities in the scene based on the trajectory data.