Next Article in Journal
Design and Implementation of 2.45 GHz Passive SAW Temperature Sensors with BPSK Coded RFID Configuration
Next Article in Special Issue
Comparative Evaluation of Background Subtraction Algorithms in Remote Scene Videos Captured by MWIR Sensors
Previous Article in Journal
Slippage Detection with Piezoresistive Tactile Sensors
Previous Article in Special Issue
A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(8), 1845; https://doi.org/10.3390/s17081845

Headgear Accessories Classification Using an Overhead Depth Sensor

Department of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares, Spain
*
Author to whom correspondence should be addressed.
Received: 22 June 2017 / Revised: 2 August 2017 / Accepted: 8 August 2017 / Published: 10 August 2017
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
View Full-Text   |   Download PDF [10711 KB, uploaded 10 August 2017]   |  

Abstract

In this paper, we address the generation of semantic labels describing the headgear accessories carried out by people in a scene under surveillance, only using depth information obtained from a Time-of-Flight (ToF) camera placed in an overhead position. We propose a new method for headgear accessories classification based on the design of a robust processing strategy that includes the estimation of a meaningful feature vector that provides the relevant information about the people’s head and shoulder areas. This paper includes a detailed description of the proposed algorithmic approach, and the results obtained in tests with persons with and without headgear accessories, and with different types of hats and caps. In order to evaluate the proposal, a wide experimental validation has been carried out on a fully labeled database (that has been made available to the scientific community), including a broad variety of people and headgear accessories. For the validation, three different levels of detail have been defined, considering a different number of classes: the first level only includes two classes (hat/cap, and no hat/cap), the second one considers three classes (hat, cap and no hat/cap), and the last one includes the full class set with the five classes (no hat/cap, cap, small size hat, medium size hat, and large size hat). The achieved performance is satisfactory in every case: the average classification rates for the first level reaches 95.25%, for the second one is 92.34%, and for the full class set equals 84.60%. In addition, the online stage processing time is 5.75 ms per frame in a standard PC, thus allowing for real-time operation. View Full-Text
Keywords: headgear accessories classification; time-of-flight sensor; feature extraction; semantic features; depth maps; overhead camera headgear accessories classification; time-of-flight sensor; feature extraction; semantic features; depth maps; overhead camera
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Luna, C.A.; Macias-Guarasa, J.; Losada-Gutierrez, C.; Marron-Romera, M.; Mazo, M.; Luengo-Sanchez, S.; Macho-Pedroso, R. Headgear Accessories Classification Using an Overhead Depth Sensor. Sensors 2017, 17, 1845.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top