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

Öz

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. 

Anahtar Kelimeler

• Pedestrian attribute recognition,deep learning,feature extraction

Yaya Özellik Tanıma için LM Filtre Temelli Derin Evrişimsel Sinir Ağı

Öz

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.

Anahtar Kelimeler

• Yaya özellik tanıma,derin öğrenme,öznitelik çıkartma

Kaynakça

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