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Related Experiment Video

Updated: Feb 7, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

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Published on: April 13, 2016

An Occlusion-Robust Feature Selection Framework in Pedestrian Detection ^†.

Zhixin Guo¹, Wenzhi Liao², Yifan Xiao³

¹Department of Telecommunications and Information Processing, Ghent University-Interuniversitair Micro-Elektronica Centrum (IMEC), Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium. zhixin.guo@ugent.be.

Sensors (Basel, Switzerland)

|July 18, 2018

Summary

This summary is machine-generated.

This study introduces a new feature selection method for pedestrian detection. It improves performance, especially for occluded pedestrians, by reducing feature noise and redundancy.

Keywords:

deep learning feature selection occlusion handling pedestrian detection

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Published on: June 9, 2020

Area of Science:

Computer Vision
Machine Learning

Background:

Advanced features enhance pedestrian detection but introduce noise and redundancy.
High-dimensional features can hinder model learning and reduce performance.
Existing methods often rely on dimension reduction techniques.

Purpose of the Study:

To propose a novel feature selection framework for pedestrian detection.
To enhance the handling of occluded pedestrians in detection models.
To address noise and redundancy in high-dimensional feature sets.

Main Methods:

Developed a simple yet effective feature selection framework.
Introduced occluded pedestrian samples into the training dataset.
Implemented a new feature selection criterion for occlusion handling.

Main Results:

The proposed method demonstrated efficiency on the Caltech Pedestrian dataset.
Achieved superior performance compared to state-of-the-art methods.
Showcased significant improvements in detecting occluded pedestrians.

Conclusions:

The feature selection framework effectively reduces noise and redundancy.
The integration of occluded samples and a new criterion improves occlusion handling.
The method offers a practical solution for robust pedestrian detection.