Semi-supervised Learning in Camera Surveillance Image Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00356755" target="_blank" >RIV/68407700:21730/21:00356755 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ICCP53602.2021.9733483" target="_blank" >https://doi.org/10.1109/ICCP53602.2021.9733483</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCP53602.2021.9733483" target="_blank" >10.1109/ICCP53602.2021.9733483</a>

Result language

angličtina

Original language name

Semi-supervised Learning in Camera Surveillance Image Classification

Original language description

Recognizing pedestrian attributes in camera surveillance images is a very hard problem, due to the lack of high-quality labeled data. In the field of deep learning the semi-supervised learning paradigm provides a possible answer to this problem. We propose a novel semi-supervised model that we call Binary Mean Teacher, tailored for binary classification task of detecting the presence of wearable objects. We train our model in a traditional scenario with a randomly initialized model, but we also explore fine-tuning a model pretrained on a large-scale image dataset. The performance of our model is compared to strong supervised baselines trained or fine-tuned using our dataset and the same augmentation strategy as in our model. We evaluate the impact of various augmentation strategies commonly used in deep learning on the performance of models in our binary classification task. Using only 1000 labeled training images, randomly initialized Binary Mean Teacher model achieves roughly 90% classification accuracy compared to 75% accuracy of randomly initialized supervised model that does not use any augmentations. When both Binary Mean Teacher and the supervised model are pretrained using the ImageNet dataset, and augmentations are used for both models, the Binary Mean Teacher achieves 92% accuracy compared to 90% accuracy of the supervised model.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP)

ISBN

978-1-6654-0976-6

ISSN

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e-ISSN

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Number of pages

8

Pages from-to

155-162

Publisher name

IEEE Industrial Electronic Society

Place of publication

Vienna

Event location

Cluj-Napoca

Event date

Oct 28, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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