Anomaly Detection in Video Data Based on Probabilistic Latent Space Models
This addresses anomaly detection in video for autonomous vehicles, but it appears incremental as it builds on existing VAE and particle filter methods.
The paper tackles anomaly detection in video data for autonomous vehicles by using a Variational Autoencoder to reduce dimensionality and an Adapted Markov Jump Particle Filter for prediction, achieving detection in semi-autonomous vehicle tasks in a closed environment.
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.