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Skeleton-Based Attention Mask for Pedestrian Attribute Recognition Network.

Sorn Sooksatra¹, Sitapa Rujikietgumjorn¹

¹National Electronic and Computer Technology Center, National Science and Technology Development Agency, Pathum Thani 12120, Thailand.

Journal of Imaging

|December 23, 2021

Summary

This summary is machine-generated.

This study introduces a new pedestrian attribute recognition model using skeleton data and soft attention. The enhanced network improves classification accuracy, especially for local attributes and diverse human postures.

Keywords:

attention network pedestrian attribute recognition pose estimation

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Pedestrian attribute recognition is crucial for intelligent systems.
Existing methods struggle with variations in human posture and local attribute extraction.
Skeleton data offers a robust representation for human pose.

Purpose of the Study:

To develop an advanced pedestrian attribute recognition network.
To leverage skeleton data with soft attention for improved feature extraction.
To enhance robustness against posture variations and overfitting.

Main Methods:

Utilized skeleton data as a soft attention mechanism.
Designed attention masks for partial and whole-body focus.
Integrated an augmented layer for data augmentation to mitigate overfitting.
Evaluated the model on RAP and PETA datasets using ResNet-50, Inception V3, and Inception-ResNet V2 backbones.

Main Results:

Achieved improved overall classification performance.
Demonstrated a mean accuracy increase of approximately 2-3% with identical backbone networks.
Showcased significant improvements in recognizing local attributes.
Successfully handled variations in human posture.

Conclusions:

The proposed soft attention model effectively extracts relevant local features for pedestrian attribute recognition.
The network architecture enhances robustness and reduces overfitting.
The approach offers a promising direction for improving pedestrian analysis in complex scenarios.