Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/FD430505-1CC4-40D7-A56C-DE89E2CEC1B0 | ||
| Year | 2016 | ||
| Type of Item | Conference Full Paper | ||
| License |
|
||
| Bibliographic Citation | 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 https://doi.org/10.1109/ICIP.2016.7532630 | ||
| Appears in Collections |
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.
Copyright © DIAS 2013
