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VEDI: Vision Exploitation for Data Interpretation · 2019. 9. 11. · Method F1 score 57-POI 0,59...
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Results System and Services VEDI: Vision Exploitation for Data Interpretation Farinella G. M., Signorello G., Battiato S., Furnari A., Ragusa F., Leonardi R., Ragusa E., Scuderi E., Lopes A., Santo L., Samarotto M. DMI – IPLab, CUTGANA, University of Catania Xenia Gestione Documentale s.r.l. – Xenia Progetti s.r.l. http://iplab.dmi.unict.it/fpv References Method F1 score 57-POI 0,59 57-POI-N 0,62 9-Classifiers 0,66 Proposed 0,68 [1] Ragusa F., Furnari A., Battiato S., Signorello G., Farinella G. M., Egocentric Visitors Localization in Cultural Sites, in ACM Journal on Computing and Cultural Heritage (JOCCH), 2019. [2] Ragusa F., Furnari A., Battiato S. , Signorello G., Farinella G. M., Egocentric Point of Interest Recognition in Cultural Sites. In 14th International Conference on Computer Vision Theory and Applications (VISAPP), Prague, Czech Republic, February 25-27, 2019. [3] F. L. M. Milotta, A. Furnari, S. Battiato, M. De Salvo, G. Signorello, G. M. Farinella. Visitors Localization in Natural Sites Exploiting EgoVIsion and GPS. In 14th International Conference on Computer Vision Theory and Applications (VISAPP), Prague, Czech Republic, February 25-27, 2019. [4] Website: http ://iplab.dmi.unict.it/VEDI_project/ VEDI is an integrated system which includes a wearable device capable of supporting the visitors of cultural sites, as well as a back-end to analyze the visual information collected by the wearable system and infer behavioral information useful for the site manager. Architecture The VEDI system is made up of 4 components: 1) Mobile devices, 2) GPU, 3) Charging and update station, 4) Central system. HoloLens GoPro mFF1 0,82 0,81 mASF1 0,71 0,71 Datasets Localization and PoIs Recognition on UNICT-VEDI Monastero dei Benedettini Orto Botanico - 9 Environments (temporal annotations) - 57 Points of Interest (54248 frames with temporal and bounding boxes annotations) UNICT-VEDI UNICT-VEDI_Succulente EgoNature - 16 Points of Interest (36728 labeled images) - 9 Environments (temporal annotations and GPS locations) Accuracy Time (ms) SqueezeNet-6 + DCT 0,86 4,7 SqueezeNet-9 + DCT 0,86 6,09 SqueezeNet-11 + DCT 0,86 6,60 SqueezeNet + DCT 0,91 22,9 Localization on EgoNature Method F1 score AlexNet 0,89 PoIs Recognition on UNICT-VEDI_Succulente
Transcript of VEDI: Vision Exploitation for Data Interpretation · 2019. 9. 11. · Method F1 score 57-POI 0,59...
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Results
System and Services
VEDI: Vision Exploitation for Data InterpretationFarinella G. M., Signorello G., Battiato S., Furnari A., Ragusa F.,
Leonardi R., Ragusa E., Scuderi E., Lopes A., Santo L., Samarotto M.
DMI – IPLab, CUTGANA, University of Catania
Xenia Gestione Documentale s.r.l. – Xenia Progetti s.r.l.
http://iplab.dmi.unict.it/fpv
References
Method F1 score
57-POI 0,59
57-POI-N 0,62
9-Classifiers 0,66
Proposed 0,68
[1] Ragusa F., Furnari A., Battiato S., Signorello G., Farinella G. M.,
Egocentric Visitors Localization in Cultural Sites, in ACM Journal on
Computing and Cultural Heritage (JOCCH), 2019.
[2] Ragusa F., Furnari A., Battiato S. , Signorello G., Farinella G. M.,
Egocentric Point of Interest Recognition in Cultural Sites. In 14th
International Conference on Computer Vision Theory and
Applications (VISAPP), Prague, Czech Republic, February 25-27,
2019.
[3] F. L. M. Milotta, A. Furnari, S. Battiato, M. De Salvo, G.
Signorello, G. M. Farinella. Visitors Localization in Natural Sites
Exploiting EgoVIsion and GPS. In 14th International Conference on
Computer Vision Theory and Applications (VISAPP), Prague, Czech
Republic, February 25-27, 2019.
[4] Website: http://iplab.dmi.unict.it/VEDI_project/
VEDI is an integrated system which includes a wearable device capable of
supporting the visitors of cultural sites, as well as a back-end to analyze the
visual information collected by the wearable system and infer behavioral
information useful for the site manager.
Architecture
The VEDI system is made up of 4 components: 1) Mobile
devices, 2) GPU, 3) Charging and update station, 4)
Central system.
HoloLens GoPro
mFF1 0,82 0,81
mASF1 0,71 0,71
Datasets
Localization and PoIs Recognition on UNICT-VEDI
Monastero dei Benedettini Orto Botanico
- 9 Environments (temporal annotations)
- 57 Points of Interest (54248 frames with temporal and
bounding boxes annotations)
UNICT-VEDI
UNICT-VEDI_Succulente
EgoNature
- 16 Points of Interest (36728 labeled
images)
- 9 Environments (temporal
annotations and GPS locations)
Accuracy Time (ms)
SqueezeNet-6 + DCT 0,86 4,7
SqueezeNet-9 + DCT 0,86 6,09
SqueezeNet-11 + DCT 0,86 6,60
SqueezeNet + DCT 0,91 22,9
Localization on EgoNature
Method F1 score
AlexNet 0,89
PoIs Recognition on UNICT-VEDI_Succulente
http://iplab.dmi.unict.it/fpvhttp://iplab.dmi.unict.it/VEDI_POIs/
