Human action recognition using 4W1H and Particle Swarm Optimization Clustering

Tracking and recording human activities have been a major interest in the iSpace, for this purpose different recognition and clustering techniques have been used, like using a Learning Classifier System and data Mining Techniques. These techniques share the common factor of database dependence and there was actually little effort into making the system to understand the way human were behaving in a given time in the space. Using Artificial Intelligence techniques, we present a work that reads and classifies user object activity.