Anomaly Detection in Video Data Based on Probabilistic Latent Space Models

• URL: http://arxiv.org/abs/2003.07623v1
• Date: Tue, 17 Mar 2020 10:32:22 GMT
• Title: Anomaly Detection in Video Data Based on Probabilistic Latent Space Models
• Authors: Giulia Slavic, Damian Campo, Mohamad Baydoun, Pablo Marin, David Martin, Lucio Marcenaro, Carlo Regazzoni
• Abstract summary: A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.
• Score: 7.269230232703388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

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