Source of Publication
Keeping an eye on pedestrians as they navigate through a scene, surveillance cameras are everywhere. 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 multi-label problem is extremely challenging even for human observers and has rightly garnered attention from the computer vision community. Towards a solution to this problem, in this paper, we adopt trainable Gabor wavelets (TGW) layers and cascade them 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 two-branch neural network where mixed layers, a combination of the TGW and convolutional layers, make up the building block of our deep neural network. We test our method on twoo challenging publicly available datasets and compare our results with state of the art.
Public Library of Science (PLoS)
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Junejo, Imran N., "Pedestrian attribute recognition using two-branch trainable Gabor wavelets network" (2021). All Works. 4283.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series