We review the Distributional Clauses Particle Filter (DCPF), a statistical relational framework for inference in hybrid domains overtime such as vision and robotics. Applications in these domains are challenging for statistical relational learning as they require dealing with continuous distributions and dynamics in real-time. The framework addresses these issues, it supports the online learning of parameters and it was tested in several tracking scenarios with good results.