Thesis Details
Charakterizace chodců ve videu
This work deals with obtaining pedestrian information, which are captured by static, external cameras located in public, outdoor or indoor spaces. The aim is to obtain as much information as possible. Information such as gender, age and type of clothing, accessories, fashion style, or overall personality are obtained using using convolutional neural networks. One part of the work consists of creating a new dataset that captures pedestrians and includes information about the person's sex, age, and fashion style. Another part of the thesis is the design and implementation of convolutional neural networks, which classify the mentioned pedestrian characteristics. Neural networks evaluate pedestrian input images in PETA, FashionStyle14 and BUT Pedestrian Attributes datasets. Experiments performed over the PETA and FashionStyle datasets compare my results to various convolutional neural networks described in publications. Further experiments are shown on created BUT data set of pedestrian attributes.
image recognition, convolutional neural networks, transfered learning, feature extractor, fine-tuning, resnet, attribute classification, dataset, BUT atribúty chodocov, PETA, FashionStyle14
Bidlo Michal, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Grézl František, Ing., Ph.D. (DCGM FIT BUT), člen
Lucká Mária, prof. RNDr., Ph.D. (FIIT STU), člen
Rogalewicz Adam, doc. Mgr., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT22073,
author = "Zuzana Studen\'{a}",
type = "Master's thesis",
title = "Charakterizace chodc\r{u} ve videu",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2019,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/22073/"
}