URI | http://purl.tuc.gr/dl/dias/FD430505-1CC4-40D7-A56C-DE89E2CEC1B0 | - |
Αναγνωριστικό | https://ieeexplore.ieee.org/document/7532630/ | - |
Αναγνωριστικό | https://doi.org/10.1109/ICIP.2016.7532630 | - |
Γλώσσα | en | - |
Μέγεθος | 5 pages | en |
Τίτλος | Deep learning based human behavior recognition in industrial workflows | en |
Δημιουργός | Makantasis Konstantinos | en |
Δημιουργός | Μακαντασης Κωνσταντινος | el |
Δημιουργός | Doulamis Anastasios | en |
Δημιουργός | Δουλαμης Αναστασιος | el |
Δημιουργός | Doulamis Nikolaos D. | en |
Δημιουργός | Psychas Konstantinos | en |
Δημιουργός | Ψυχας Κωνσταντινος | el |
Εκδότης | Institute of Electrical and Electronics Engineers | en |
Περίληψη | We consider the fully automated behavior understanding through visual cues in industrial environments. In contrast to most existing work, which relies on domain knowledge to construct complex handcrafted features from inputs, we exploit a Convolutional Neural Network (CNN), which is a type of deep model and can act directly on the raw inputs, to automate the process of feature construction. Although such models are limited to handle still 2D inputs, in this paper we appropriately transform video input to incorporate temporal information into each frame. This way our model hierarchically constructs features from both spatial and temporal dimensions. We apply our model in real-world environment, on data taken from Nissan factory, and it achieves superior performance without relying on handcrafted features. | en |
Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
Τύπος | Conference Full Paper | en |
Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
Ημερομηνία | 2018-10-03 | - |
Ημερομηνία Δημοσίευσης | 2016 | - |
Θεματική Κατηγορία | Behavior understanding | en |
Θεματική Κατηγορία | Convolutional neural networks | en |
Θεματική Κατηγορία | Deep learning | en |
Θεματική Κατηγορία | Industrial workflow | en |
Βιβλιογραφική Αναφορά | K. Makantasis, A. Doulamis, N. Doulamis and K. Psychas, "Deep learning based human behavior recognition in industrial workflows," in 23rd IEEE International Conference on Image Processing, 2016, pp. 1609-1613. doi: 10.1109/ICIP.2016.7532630
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