  This paper proposes a method for performing continual learning of predictive
models that facilitate the inference of future frames in video sequences. For a
first given experience, an initial Variational Autoencoder, together with a set
of fully connected neural networks are utilized to respectively learn the
appearance of video frames and their dynamics at the latent space level. By
employing an adapted Markov Jump Particle Filter, the proposed method
recognizes new situations and integrates them as predictive models avoiding
catastrophic forgetting of previously learned tasks. For evaluating the
proposed method, this article uses video sequences from a vehicle that performs
different tasks in a controlled environment.
