The paradigm of cognitive dynamic systems (CDSs) can provide a framework under which a continuously learning cognitive module can be designed.  In particular, CDS theory describes a basic vocabulary of components that can be used as the founding blocks of a module capable to learn behavioral rules from continuous active interactions with the environment.  In this paper we propose a general CDS-based approach to tracking.  We show that such a CDS-inspired design can lead to the self-adaptability of a Bayesian tracker in fusing heterogeneous object features, overcoming size change issues. 
CDS
In this paper we propose a general CDS-based approach to tracking. 
CDS-based approach
We show that such a CDS-inspired design can lead to the self-adaptability of a Bayesian tracker in fusing heterogeneous object features, overcoming size change issues. 
Bayesian tracker