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Pedestrian attribute recognition using trainable Gabor wavelets.

Imran N Junejo1, Naveed Ahmed2, Mohammad Lataifeh2

  • 1Zayed University, Dubai, United Arab Emirates.

Heliyon
|July 14, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces trainable Gabor wavelet layers within convolution neural networks for improved pedestrian attribute recognition (PAR). The novel approach enhances accuracy in identifying human characteristics from surveillance footage.

Keywords:
Attribute recognitionComputer visionDeep learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Pedestrian attribute recognition (PAR) is crucial for surveillance systems.
  • Current methods face challenges in accurately identifying diverse attributes like age and clothing.
  • PAR is a complex multi-label problem, demanding advanced recognition techniques.

Purpose of the Study:

  • To enhance pedestrian attribute recognition (PAR) accuracy.
  • To introduce a novel deep learning approach integrating trainable Gabor wavelet layers into CNNs.
  • To improve the adaptability and performance of PAR systems.

Main Methods:

  • Integration of trainable Gabor wavelet (TGW) layers within a convolution neural network (CNN) architecture.
  • Development of learnable Gabor filters that adapt to specific datasets, unlike fixed filters.
  • Testing the proposed TGW-CNN model on challenging, publicly available datasets.

Main Results:

  • Demonstrated considerable improvements over existing state-of-the-art approaches in PAR.
  • Showcased the effectiveness of adaptive, trainable Gabor filters for attribute recognition.
  • Achieved enhanced accuracy in extracting attributes such as age group, clothing, and accessories.

Conclusions:

  • The proposed TGW-CNN model offers a significant advancement in pedestrian attribute recognition.
  • Trainable Gabor wavelets provide a more effective feature extraction mechanism for PAR tasks.
  • This research contributes to more robust and accurate surveillance and analysis systems.