Retail inventory shrinkage reduction via action recognition

A system comprising:

• one or more processors;
• one or more memory devices storing instructions thereon, that when executed by the one or more processors, cause the one or more processors to execute operations comprising:
• processing video data, by one or more auxiliary branches of a network, wherein the video data includes images of a scene in a retail environment;
• identifying, by the one or more auxiliary branches, intermediate auxiliary features corresponding to the video data, wherein the intermediate auxiliary features include subject features corresponding to a subject in the scene of the retail environment and scene features corresponding to the scene of the retail environment;
• integrating intermediate action features corresponding to subject movements from an action branch and the intermediate auxiliary features from the one or more auxiliary branches of the network;
• generating, based on the integrated intermediate action features and intermediate auxiliary features, a set of high-level action features corresponding to the subject movements by the action branch;
• integrating high-level auxiliary features from the one or more auxiliary branches and the high-level action features from the action branch;
• classifying a reportable action based on the integrated high-level auxiliary features and the high-level action features, wherein the reportable action is associated with an inventory shrinkage event;
• based on the classified reportable action, determining the subject identified in the video data has performed the reportable action;
• and
• providing an indicator to a user, wherein the indicator includes information relating to the inventory shrinkage event.

One or more non-transitory computer storage media having computer-executable instructions embodied thereon that, when executed, by one or more processors, cause the one or more processors to perform a method for implementing action recognition systems, the method comprising:

• processing video data, by one or more auxiliary branches of a network;
• identifying, by the one or more auxiliary branches, intermediate auxiliary features corresponding to the video data;
• integrating intermediate action features from an action branch and the intermediate auxiliary features from the one or more auxiliary branches of the network;
• generating, based on the integrated intermediate action features and intermediate auxiliary features, a set of high-level action features by the action branch;
• integrating high-level auxiliary features from the one or more auxiliary branches and the high-level action features from the action branch;
• and
• classifying an action based on the integrated high-level auxiliary features and the high-level action features.