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.
This paper proposes a method for performing future-frame prediction and anomaly detection on video data in a multi-modal framework based on Dynamic Bayesian Networks (DBNs). In particular, odometry data and video data from a moving vehicle are fused. A Markov Jump Particle Filter (MJPF) is learned on odometry data, and its features are used to aid the learning of a Kalman Variational Autoencoder (KVAE) on video data. Consequently, anomaly detection can be performed on video data using the learned model. We evaluate the proposed method using multi-modal data from a vehicle performing different tasks in a closed environment.
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.
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