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

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 1 x 256 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.