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