Volume 11, Number 15, September 2021

Pedestrian Attribute Recognition using Gabor Wavelet Layers

  Authors

Imran N. Junejo, Zayed University, UAE

  Abstract

We address the problem of Pedestrian Attribute Recognition (PAR) in this paper. Owing to the presence of surveillance cameras in almost all outdoor and indoor public spaces, keeping and eye on pedestrian is a sought-after task with many useful applications. The problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem and challenging even for human observers. We propose using a convolution neural network (CNN) with trainable Gabor wavelets (TGW) layers. The proposed layers are learnable and adapt to the dataset for a better recognition. The proposed multi-branch neural network is a mix of TGW and convolutional layers and we show its effectiveness on a public dataset.

  Keywords

Gabor Wavelets, Convolutional Neural Networks, Pedestrian Attributes.