Novel Approach for Person Detection Based on Image Segmentation Neural Network

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F20%3A39916808" target="_blank" >RIV/00216275:25530/20:39916808 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-57802-2_16" target="_blank" >http://dx.doi.org/10.1007/978-3-030-57802-2_16</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-57802-2_16" target="_blank" >10.1007/978-3-030-57802-2_16</a>

Result language
angličtina
Original language name
Novel Approach for Person Detection Based on Image Segmentation Neural Network
Original language description
With the rise of the modern possibilities in computer science and device engineering, as well as with growing population in big cities among the world, a lot of new approaches for person detection have become a very interesting topic. In this paper, two different approaches for person detection are tested and compared. As the first and standard approach, the YOLO architectures, which are very effective for image classification, are adapted to the detection problem. The second and novel approach is based on the encoder-decoder scheme causing the image segmentations, in combination with the locator. The locator part is supposed to find local maxima in segmented image and should return the specific coordinates representing the head centers in the original image. Results clearly report this approach with U-Net used as encoder-decoder scheme with the locator based on local peaks as the more accurately performing detection technique, in comparison to YOLO architectures.
Czech name
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Czech description
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Project
<a href="/en/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)
ISBN
978-3-030-57801-5
ISSN
2194-5357
e-ISSN
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Number of pages
10
Pages from-to
166-175
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Burgos
Event date
Sep 16, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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