Action classification of 3D human models using dynamic ANNs for mobile robot surveillance

This paper presents an alternative approach on physical human action classification implemented by mobile robots. In contrast with other action recognition methods, this research indicates the best configuration topology of a number of dynamic neural networks to be used in 3D time series classification by showing several comparison performances. In this action recognition investigation we demonstrate high level network granularity on dynamic classification and class discrimination of normal and aggressive action recognition. An interconnection between an ubiquitous 3D sensory tracker system and a mobile robot is set to create a perception to action architecture capable to perceive, process, and classify physical human actions. The robot is used as a process-to-action unit to process the 3D data taken by the tracker and to eventually generate surveillance assessment reports pointing towards action-class matchings as well as generating evaluation statistics which signify the quality of the actions recognized.