Detecting Wearable Objects via Transfer Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00337709" target="_blank" >RIV/68407700:21730/19:00337709 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICCP48234.2019.8959621" target="_blank" >https://doi.org/10.1109/ICCP48234.2019.8959621</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCP48234.2019.8959621" target="_blank" >10.1109/ICCP48234.2019.8959621</a>
Alternative languages
Result language
angličtina
Original language name
Detecting Wearable Objects via Transfer Learning
Original language description
Transfer learning is a well known technique to circumvent the problem of small datasets in deep machine learning. It has been successfully used in the field of camera surveillance image processing which suffers from poor data quality and quantity. We focused on the task of wearable object detection, namely distinguishing if a person is or is not wearing a backpack. We created new annotations for the DukeMTMC-attribute dataset to overcome the discrepancies among the attributes. We explored transfer learning with a frozen feature extractor as well as the model fine-tuning, which turned out to perform much better. In both setups we found that the Densenet161 is the best from tested architectures. Our best model achieved about 92% balanced accuracy on the testing set.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
<a href="/en/project/VI20172019082" target="_blank" >VI20172019082: Smart Camera - New Generation Monitoring Centre</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP)
ISBN
978-1-7281-4914-1
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
373-380
Publisher name
IEEE
Place of publication
Piscataway, NJ
Event location
Cluj-Napoca
Event date
Sep 5, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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