Aydin, Burak

Profile Picture
Profile URL
https://gcris.yasar.edu.tr/handle/123456789/13469
Name Variants
Job Title
Öğrt.Gör.
Email Address
Main Affiliation
01.01.15.01. İngilizce Hazırlık Sınıfı Programı
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
XT0VS_oAAAAJ
WoS Researcher ID

Sustainable Development Goals

SDG data is not available
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

1

Articles

1

Views / Downloads

0/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

27

Scopus Citation Count

34

Patents

0

Projects

0

WoS Citations per Publication

27.00

Scopus Citations per Publication

34.00

Open Access Source

1

Supervised Theses

0

JournalCount
IEEE Transactions on Vehicular Technology1
Current Page: 1 / 1

Scopus Quartile Distribution

Quartile distribution chart data is not available

Competency Cloud

GCRIS Competency Cloud

Filters

20201
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Article
    Citation - WoS: 27
    Citation - Scopus: 34
    Conditional Weighted Ensemble of Transferred Models for Camera Based Onboard Pedestrian Detection in Railway Driver Support Systems
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020) Tugce Toprak; Burak Belenlioglu; Burak Aydin; Cuneyt Guzelis; M. Alper Selver; Toprak, Tugce; Guzelis, Cuneyt; Selver, M. Alper; Belenlioglu, Burak; Aydin, Burak
    Pedestrian Detection (PD) is one of the most studied issues of driver assistance systems. Although a tremendous effort is already given to create datasets and to develop classifiers for cars studies about railway systems remain very limited. This article shows that direct application of neither existing advanced object detectors (such as AlexNet VGG YOLOetc.) nor specifically created systems for PD(such as Caltech/INRIA trained classifiers) can provide enough performance to overcome railway specific challenges. Fortunately it is also shown that without waiting the collection of a mature dataset for railways as comprehensively diverse and annotated as the existing ones for cars a Transfer Learning (TL) approach to fine-tune various successful deep models (pre-trained using both extensive image and pedestrian datasets) to railway PD tasks provides an effective solution. To achieve TL a new RAil-Way PEdestrian Dataset (RAWPED) is collected and annotated. Then a novel three-stage system is designed. At its first stage a feature-classifier fusion is created to overcome the localization and adaptation limitations of deep models. At the second stage the complementarity of the transferred models and diversity of their results are exploited by conducted measurements and analyses. Based on the findings at the third stage a novel learning strategy is developed to create an ensemble which conditionally weights the outputs of individual models and performs consistently better than its components. The proposed system is shown to achieve a log average miss rate of 0.34 and average precision of 0.93 which are significantly better than the performance of compared well-established models.