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Abstract
In this work, we provide a framework for recognizing human behavior from multiple cameras in structured industrial environments. 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. Modeling and recognition are performed through the use of Hidden Markov Models (HMMs) with Gaussian observation likelihoods. The problems of limited visibility and occlusions are addressed by showing how the framework can be extended 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 discuss the obtained results.
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Authors and Affiliations
N.C.S.R. “Demokritos” Institute of Inform. and Telecom., 15310, Aghia Paraskevi, Greece
Dimitrios I. Kosmopoulos
National Technical University of Athens School of Electr. and Comp. Enginnering, 15773, Zografou, Greece
Athanasios S. Voulodimos & Theodora A. Varvarigou
- Dimitrios I. Kosmopoulos
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- Athanasios S. Voulodimos
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Editors and Affiliations
Institute of Informatics and Telecommunications, NCSR Demokritos, Ag. Paraskevi, 15310, Athens, Greece
Stasinos Konstantopoulos , Stavros Perantonis , Vangelis Karkaletsis & Constantine D. Spyropoulos , , &
Department of Information and Communication Systems Engineering, University of the Aegean, 83200, Karlovassi, Samos, Greece
George Vouros
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Kosmopoulos, D.I., Voulodimos, A.S., Varvarigou, T.A. (2010). Behavior Recognition from Multiple Views Using Fused Hidden Markov Models. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_41
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