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Related Experiment Video

Updated: Aug 29, 2025

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

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Occluded Pedestrian-Attribute Recognition for Video Sensors Using Group Sparsity.

Geonu Lee¹, Kimin Yun², Jungchan Cho¹

¹College of Information Technology, Gachon University, Sengnam 13120, Korea.

Sensors (Basel, Switzerland)

|September 9, 2022

Summary

This summary is machine-generated.

This study introduces a novel group sparsity temporal attention module to improve pedestrian-attribute recognition (PAR) in occluded scenarios. The method enhances accuracy by correlating attributes, overcoming limitations of previous attention-based approaches.

Keywords:

deep learning group-sparsity loss temporal attention module video-based pedestrian-attribute recognition

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Pedestrian-attribute recognition (PAR) is crucial for intelligent systems but is challenged by occlusions in real-world vision sensors.
Existing PAR methods struggle with occlusions, leading to difficulties in recognizing attributes when pedestrians are partially hidden.

Purpose of the Study:

To develop a robust method for pedestrian-attribute recognition (PAR) that effectively handles occlusions.
To address the issue of uncorrelated attribute recognition during occlusion by incorporating attribute correlations.

Main Methods:

A novel temporal-attention module based on group sparsity was proposed to improve PAR under occlusion.
Group sparsity was applied across attention weights of correlated attributes, forcing attention to focus on the same frames for physically-adjacent attributes.

Main Results:

The proposed method demonstrated significant improvements in F1-scores on occlusion samples compared to advanced baseline methods.
Achieved 1.18% and 6.21% higher F1-scores on the DukeMTMC-VideoReID and MARS datasets, respectively.

Conclusions:

The group sparsity temporal attention module effectively enhances pedestrian-attribute recognition in the presence of occlusions.
This approach successfully addresses the challenge of recognizing correlated attributes simultaneously, even when partially obscured.