Robust Human Behavior Modeling from Multiple Cameras

In this work, we propose a framework for classifying structured human behavior in complex real environments, where problems such as frequent illumination changes and heavy occlusions are expected. Since target recognition and tracking can be very challenging, we bypass these problems by employing an approach similar to Motion History Images for feature extraction. Furthermore, to tackle outliers residing within the training data, which might affect severely the training algorithm of models with Gaussian observation likelihoods, we scrutinize the effectiveness of the multivariate Student-t distribution as the observation likelihood of the employed Hidden Markov Models. Additionally, the problem of visibility and occlusions is addressed by providing various extensions of the framework for multiple cameras, both at the feature and at the state level. Finally, we evaluate the performance of the examined approaches under real-life visual behavior understanding scenarios and we compare and discuss the obtained results.