Enhanced human behavior recognition using HMM and evaluative rectification
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Bayesian filter based behavior recognition in workflows allowing for user feedback
Computer Vision and Image Understanding
A top-down event-driven approach for concurrent activity recognition
Multimedia Tools and Applications
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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.