CULS – INDOOR OCCUPANCY DETECTION DATASET

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F23%3A96592" target="_blank" >RIV/60460709:41110/23:96592 - isvavai.cz</a>

Result on the web

<a href="https://ap.pef.czu.cz/en/r-12193-conference-proceedings" target="_blank" >https://ap.pef.czu.cz/en/r-12193-conference-proceedings</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

CULS – INDOOR OCCUPANCY DETECTION DATASET

Original language description

A new dataset for occupancy detection in smart buildings such as universities is presented in this paper. The dataset can be used to train neural network models for this task (object recognition of person’s head). Detectable space in smart buildings is defined as corridors/common areas as well as, for example, classrooms and auditoriums. New dataset is specific and unique because it contains annotations of indoor occupants from three views: front, side and back. This is different from other datasets that normally focus on only one type of annotation. The dataset also considers the varied conditions that occur during detection – for example, the positioning of cameras in overhead, from the side, or other conditions, such as lighting. In the cooperation with Security Department of Czech University of Life Sciences Prague, the video recordings of five lecture rooms were obtained for a duration of ~372 hours, from which still images were created and all persons appearing there were manually annotated with bounding boxes. The number of these annotations amounts to 10 044 persons. Comparison was then made on this data with other publicly available datasets. Then, ResNet-50 model was trained using this dataset to determine if this dataset is applicable in machine learning. It was found that a similar dataset designed primarily to count people from different perspectives in auditoriums did not exist at the time of the research. Compared to other dataset, presented dataset is smaller in size, however by creating an experimental model based on ResNet50, it was found that in transfer learning, the model created is capable of inference and is therefore practically applicable. Hence, the dataset can be used in machine learning

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

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Continuities

R - Projekt Ramcoveho programu EK

Publication year

2023

Confidentiality

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

Article name in the collection

Agrarian perspectives XXXII. Human Capital and Education in Agriculture

ISBN

978-80-213-3309-3

ISSN

2464-4781

e-ISSN

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Number of pages

13

Pages from-to

143-155

Publisher name

PEF ČZU v Praze

Place of publication

Praha

Event location

Praha

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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