Method and system for activity classification

An activity classifier system, for classifying human activities using 2D skeleton data comprising joint positions, the system comprising:

• a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and joint velocities;
• a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures;
• and
• an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions,
• wherein the first recurrent neural network is trained on data comprising 2D skeleton sequences with associated gesture labels and the second recurrent neural network is trained with a pre-trained first recurrent neutral network, and 2D skeleton sequences with associated action labels, and
• wherein the skeleton preprocessor temporally smooths the joint positions, transforms the joint positions to be relative to one of the joint positions, scales the joint positions to the height of a feature of the 2D skeleton, and computes the velocity of each joint position.