A human action classifier from 4-D data (3-D+TIME): Based on an invariant body shape descriptor and hidden markov models

Many human action definitions have been provided in the field of human computer interaction studies. These distinctions could be considered merely semantical as human actions are all carried out performing sequences of body postures. In this paper we propose a human action classifier based on volumetric reconstructed sequences (4-D data) acquired from a multi-viewpoint camera system. In order to design the most general action classifier possible, we concentrate our attention in extracting only posture-dependent information from volumetric frames and in performing action distinction only on the basis of the sequence of body postures carried out in the scene. An Invariant Shape Descriptor (ISD) is used in order to properly describe the body shape and its dynamic changes during an action execution. The ISD data is then analyzed in order to extract suitable features able to meaningfully represent a human action independently from body position, orientation, size and proportions. The action classification is performed using a supervised recognizer based on the Hidden Markov Models (HMM) theory. Experimental results, evaluated using an extensive action sequence dataset and applying different training conditions to the HMM-based classifier, confirm the reliability of the proposed approach.