Human action recognition using Recursive Self Organizing map and longest common subsequence matching

A little attention has been given to the use of recursive self organizing map (SOM) for human action recognition in the past years. This paper introduces an action recognition framework using the recursive SOM, a temporal extension of SOM that learns adapted representations of temporal context associated with a time series. We demonstrate the effectiveness of recursive SOM for data clustering, dimensionality reduction and context learning in human action recognition. The atomic poses in motion sequences and their contextual information are extracted and encoded by the trained recursive SOM. A human action sequence is represented as a trajectory of map units. To classify a new action, a longest common subsequence algorithm using dynamic programming is employed to robustly match action trajectories on the map. To the best of our knowledge, we are the first to try recursive SOM approach for human action recognition. We test the approach on a well known benchmark action dataset and achieve promising results.