Source of Publication
CCBYNCND 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.
Institute of Electrical and Electronics Engineers (IEEE)
Cameras, Computer Vision, Computer vision, Convolution, Deep Learning, Feature extraction, Image color analysis, Pedestrian Attribute Recognition, Surveillance, Visualization
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Junejo, Imran N., "Multi-branch Gabor Wavelet Layers for Pedestrian Attribute Recognition" (2021). All Works. 4062.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series