LM Filter-Based Deep Convolutional Neural Network for Pedestrian Attribute Recognition

Year 2020, , 605 - 613, 01.09.2020

Hüseyin Üzen , Kazım Hanbay

https://doi.org/10.2339/politeknik.525600

Cited By: 4

Abstract

Today,
Convolutional Neural Network (CNN) architectures have been used actively in
many different areas such as security, industry and big data. Thanks to the
convolution layers in these architectures, they can automatically extract the
best features that can give the desired results for a classification or
definition problem. In this paper, a new Hybrid Convolutional Neural Network
(HESA) architecture is proposed to calculate both the traditional and the deep
features. The main purpose of this network architecture is to combine the traditional features obtained from the LM filters and the
deep features obtained from the CNN architecture so thus create a strong
feature data for classification. In the proposed model, the LM filter features
and deep features of the pedestrian image are calculated simultaneously. Then,
these features are combined and features vector consisting of  different features is built. This
feature vector is taken into the classification process with the help of fully
connected layer. The developed HESA architecture has been applied for the
pedestrian attribute classification which is a very difficult problem. The
proposed model significantly outperforms the SVM and MRF based methods on the
PETA database. In addition, the use of the ReduceLROnPlateau model in the HESA
method has made a significant contribution to achieving high successes. 

Keywords

Pedestrian attribute recognition, deep learning, feature extraction

Yaya Özellik Tanıma için LM Filtre Temelli Derin Evrişimsel Sinir Ağı

Abstract

Günümüzde Evrişimsel Sinir Ağı (ESA) mimarileri güvenlik, endüstri ve
büyük veri gibi birçok farklı alanda aktif olarak kullanılmaktadır. Bu
mimarilerdeki evrişim katmanları, bir sınıflandırma veya tanımlama problemi
için istenilen sonuçları verebilecek en iyi öznitelikleri otomatik olarak
çıkartabilmektedir. Bu çalışmada, hem geleneksel hem de derin öznitelikleri
hesaplamak için yeni bir Hibrit Evrişimsel Sinir Ağı (HESA) mimarisi
önerilmiştir. Bu ağ mimarisinin temel amacı, LM filtrelerinden elde edilen
geleneksel öznitelikler ile ESA mimarisinden elde edilen derin öznitelikleri birleştirerek
güçlü bir öznitelik verisi oluşturmaktır. Önerilen modelde yaya görüntüsünden
elde edilen LM filtre öznitelikleri ve derin öznitelikler eşzamanlı olarak
hesaplanmaktadır. Daha sonra bu öznitelikler birleştirilir ve  farklı öznitelikten oluşan bir
öznitelik vektörü oluşturulur. Bu öznitelik vektörü tam bağlı katmanlar yardımı
ile sınıflandırma işlemine alınır. Geliştirilen HESA mimarisi çok zor bir
problem olan yaya özellik sınıflandırması için uygulanmıştır. Önerilen model PETA
veri tabanında SVM ve MRF tabanlı yöntemlerden önemli ölçüde daha iyi
performans göstermiştir. Ayrıca, ReduceLROnPlateau modelinin HESA
yönteminde kullanılması yüksek başarıların elde edilmesine önemli bir katkı sağlamıştır.

Keywords

Yaya özellik tanıma, derin öğrenme, öznitelik çıkartma

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