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This paper proposes a method for detecting anomalies in video data. AVariational Autoencoder (VAE) is used for reducing the dimensionality of videoframes, generating latent space information that is comparable tolow-dimensional sensory data (e.g., positioning, steering angle), makingfeasible the development of a consistent multi-modal architecture forautonomous vehicles. An Adapted Markov Jump Particle Filter defined by discreteand continuous inference levels is employed to predict the following frames anddetecting anomalies in new video sequences. Our method is evaluated ondifferent video scenarios where a semi-autonomous vehicle performs a set oftasks in a closed environment.

Anomaly Detection in Video Data Based on Probabilistic Latent Space Models