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.
Learning of linear video prediction models in a multi-modal framework for anomaly detection
Giulia Slavic;Lucio Marcenaro;Carlo Regazzoni
2021-01-01
Abstract
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.File | Dimensione | Formato | |
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ICIP_2021_KVAE_Clustering.pdf
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