Abstract: In this paper the use of sequential machines for recognizing actions taken by the objects detected by a general tracking algorithm is proposed. The system may deal with the uncertainty inherent in medium-level vision data. For this purpose, fuzzification of input data is performed. Besides, this transformation allows to manage data independently of the tracking application selected and enables adding characteristics of the analyzed scenario. The representation of actions by means of an automaton and the generation of the input symbols for finite automaton depending on the object and action compared are described. The output of the comparison process between an object and an action is a numerical value that represents the membership of the object to the action. This value is computed depending on how similar the object and the action are. The work concludes with the application of the proposed technique to identify the behavior of vehicles in road traffic scenes.
Abstract: This paper examines the concept of simulation from
a modelling viewpoint. How can one Mealy machine simulate the other one? We create formalism for simulation of Mealy machines.
The injective s–morphism of the machine semigroups induces the simulation of machines [1]. We present the example of s–morphism
such that it is not a homomorphism of semigroups. The story for the
surjective s–morphisms is quite different. These are homomorphisms
of semigroups but there exists the surjective s–morphism such that it does not induce the simulation.