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Deep learning based human behavior recognition in industrial workflows

Makantasis Konstantinos, Doulamis Anastasios, Doulamis Nikolaos D., Psychas Konstantinos

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URIhttp://purl.tuc.gr/dl/dias/FD430505-1CC4-40D7-A56C-DE89E2CEC1B0-
Identifierhttps://ieeexplore.ieee.org/document/7532630/-
Identifierhttps://doi.org/10.1109/ICIP.2016.7532630-
Languageen-
Extent5 pagesen
TitleDeep learning based human behavior recognition in industrial workflowsen
CreatorMakantasis Konstantinosen
CreatorΜακαντασης Κωνσταντινοςel
CreatorDoulamis Anastasiosen
CreatorΔουλαμης Αναστασιοςel
CreatorDoulamis Nikolaos D.en
CreatorPsychas Konstantinosen
CreatorΨυχας Κωνσταντινοςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryWe 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
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-10-03-
Date of Publication2016-
SubjectBehavior understandingen
SubjectConvolutional neural networksen
SubjectDeep learningen
SubjectIndustrial workflowen
Bibliographic CitationK. 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 en

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