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Related Experiment Video

Updated: Feb 24, 2026

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

Published on: September 21, 2017

Headgear Accessories Classification Using an Overhead Depth Sensor.

Carlos A Luna¹, Javier Macias-Guarasa², Cristina Losada-Gutierrez³

¹Department of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares, Spain. carlos.luna@uah.es.

Sensors (Basel, Switzerland)

|August 11, 2017

Summary

This study introduces a novel method for classifying headgear accessories using only overhead Time-of-Flight (ToF) camera depth data. The system achieves high accuracy in real-time, enabling effective surveillance applications.

Keywords:

depth maps feature extraction headgear accessories classification overhead camera semantic features time-of-flight sensor

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Area of Science:

Computer Vision
Machine Learning
Surveillance Technology

Background:

Overhead surveillance systems often lack detailed human accessory analysis.
Distinguishing headgear solely from depth data presents a significant challenge.

Purpose of the Study:

To develop and validate a method for semantic headgear accessory classification using Time-of-Flight (ToF) camera depth information.
To enable real-time, overhead-based identification of headwear in surveillance scenarios.

Main Methods:

A robust processing strategy was designed, estimating feature vectors from head and shoulder areas using ToF depth data.
Classification was performed at three levels: 2-class (hat/cap vs. none), 3-class (hat, cap, none), and 5-class (detailed hat sizes).

Main Results:

The method achieved high classification rates: 95.25% (2-class), 92.34% (3-class), and 84.60% (5-class).
Processing time was 5.75 ms per frame on a standard PC, facilitating real-time operation.

Conclusions:

The proposed method effectively classifies headgear accessories from overhead ToF depth data.
The system demonstrates potential for real-time surveillance applications requiring detailed accessory analysis.