Multi-Branch Gabor Wavelet Layers for Pedestrian Attribute Recognition

Surveillance cameras are everywhere, keeping an eye on pedestrians as they navigate through a scene. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This 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. The problem has rightly attracted attention recently from the computer vision community. In this paper, we adopt trainable Gabor wavelets (TGW) layers and use it with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We propose a multi-branch neural network where mixed-layers, a combination of the TGW and convolutional layer, make up the building block of our 3-branch deep neural network. We test our method on publicly available challenging datasets and compare our results with state of the art.

Automated summary

The paper addresses the problem of pedestrian attribute recognition, proposing a method using trainable Gabor wavelet layers combined with convolution neural network. A multi-branch neural network is designed for this purpose. Testing on challenging datasets and comparing with state of the art validates the effectiveness of the proposed method.