Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q2
Comparison of ML algorithms to distinguish between human or human-like targets using the HOG features of range-time and range-Doppler images in through-the-wall applications
Turkish Journal of Electrical Engineering and Computer Sciences · Ocak 2022
YÖKSİS Kayıtları
Comparison of ML algorithms to distinguish between human or human-like targets using the HOG features of range-time and range-Doppler images in through-the-wall applications
Turkish Journal of Electrical Engineering and Computer Sciences · 2022 SCI-Expanded
Dr. Öğr. Üyesi YUNUS EMRE ACAR →
Comparison of ML algorithms to distinguish between human or human-like targets using the HOG features of range-time and range-Doppler images in through-the-wall applications
Turkish Journal of Electrical Engineering and Computer Sciences · 2022 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
A fuzzy rule based system for predicting the live weight of Holstein cows whose body dimensions were determined by image analysis
2011 ISSN: 1300-0632 SCI-Expanded
Prof. Dr. ŞAKİR TAŞDEMİR →
A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization)
2016 ISSN: 13000632 SCI-Expanded
Doç. Dr. TAHİR SAĞ →
A new ABC based multiobjective optimization algorithm with an improvement approach IBMO improved bee colony algorithm for multiobjective optimization
2016 ISSN: 13000632 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
A Control Scheme Employing an Adaptive Hysteresis Current Controller and an Uncomplicated Reference Current Generator for a Single-Phase Shunt Active Power Filter
2014 ISSN: 1300-0632 SCI-Expanded
Dr. Öğr. Üyesi HÜSEYİN DOĞAN →
An artificial neural network approach for sensorless speed estimation via rotor slot harmonics
2014 ISSN: 1300-0632 SCI-Expanded
Prof. Dr. HAYRİ ARABACI →
Modeling and evaluation of SOC-based coordinated EV charging for power
management in a distribution system
2022 ISSN: 1300-0632 SCI-Expanded Q4
Dr. Öğr. Üyesi MURAT AKIL →
The analysis and optimization of CNN Hyperparameters with fuzzy tree model for image classification
2022 ISSN: 1300-0632 Inspec, Scopus, Ei Compendex, Engineering Source, Web of Science
Doç. Dr. İLKER ALİ ÖZKAN →
The analysis and optimization of CNN Hyperparameters with fuzzy tree model
for image classification
2022 ISSN: 1300-0632 SCI Q4
Prof. Dr. ŞAKİR TAŞDEMİR →
Comparison of ML algorithms to distinguish between human or human-like targets using the HOG features of range-time and range-Doppler images in through-the-wall applications
2022 ISSN: 1300-0632 SCI-Expanded Q4
Dr. Öğr. Üyesi YUNUS EMRE ACAR →
Makale Bilgileri
ISSN13000632
Yayın TarihiOcak 2022
Cilt / Sayfa30 · 2086-2096
Scopus ID2-s2.0-85142295148
Erişim🔓 Açık Erişim
Özet
When detecting the human targets behind walls, false detections occur for many systematic and environmental reasons. Identifying and eliminating these false detections is of great importance for many applications. This study investigates the potential of machine learning (ML) algorithms to distinguish between the human and human-like targets behind walls. For this purpose, a stepped-frequency continuous-wave (SFCW) radar has been set up. Experiments have been carried out with real human targets and moving plates imitating a regular breath of a healthy human. Unlike conventional methods, human and human-like returns are classified using range-Doppler images containing range and Doppler information. Then, the histogram of oriented gradients (HOG) features of the range-Doppler images are extracted, and the number of these features is reduced by principal component analysis (PCA). Finally, popular ML algorithms are executed to distinguish the human and human-like returns. The performances of the ML algorithms are compared for both range-time and range-Doppler images with or without HOG features. Experiments have indicated that the HOG features of the range-Doppler profiles provide the best results with the support vector machine (SVM) classifier with an accuracy of 93.57%.
Yazarlar (3)
1
Yunus Emre Acar
2
Ismail Saritas
3
Ercan Yaldiz
Anahtar Kelimeler
HOG feature
human detection
machine learning
radar
through-the-wall
Kurumlar
Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Turkish Journal of Electrical Engineering and Computer Sciences
Q2
OA
SJR Skoru0,360
H-Index45
YayıncıTUBITAK
ÜlkeTurkey
Computer Science (miscellaneous) (Q2)
Electrical and Electronic Engineering (Q3)
Metrikler
1
Atıf
3
Yazar
5
Anahtar Kelime