Human action recognition based on Self Organizing Map

This paper proposes a novel neural network approach for human action recognition based on Self Organizing Map (SOM). The SOM acts as a tool to cluster feature data and to reduce data dimensionality. The key poses in action sequences are extracted by the trained SOM. After the mapping of SOM, a human action sequence is represented as a trajectory of map units. For action recognition, a longest common subsequence algorithm is utilized to match action trajectories on the map robustly. The experiments are carried out on a well known human action dataset, viz.: the Weizmann dataset. We obtain promising results which show the potential of this SOM based action recognition method.