Aerial Human Activity Recognition Through a Cognitive Architecture and a New Automata Proposal

Abstract

Video surveillance often involves several actors in multiple interactions and modelling complex activities becomes a challenge, especially in real environments. When applying autonomous video surveillance, object recognition techniques are used to produce symbolic information related to the information present in a scene. An automaton is a specialized structure capable of accepting or rejecting those symbols producing an efficient computation structure for these types of data processing. This research work presents an innovative structure for the well-known Weighted Automata to organize the information from sensors, grouping these measurements into a symbolic representation of actions that are happening in the real world. This work also proposes a hierarchical architecture formed by a multilevel sensorial system comprised of low, middle and high levels to perceive the environment and to comprehend scenes. The proposed architecture is designed to operate in a decentralized way and onboard of the Unmanned Aerial Vehicles (UAVs). The experiments showed that this system updated effectively the semantic structure given the sequence of information and demonstrated the automaton and architecture effectiveness..