Headgear Accessories Classification Using an Overhead Depth Sensor
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 accessori...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/8/1845 |
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| author | Carlos A. Luna Javier Macias-Guarasa Cristina Losada-Gutierrez Marta Marron-Romera Manuel Mazo Sara Luengo-Sanchez Roberto Macho-Pedroso |
| author_facet | Carlos A. Luna Javier Macias-Guarasa Cristina Losada-Gutierrez Marta Marron-Romera Manuel Mazo Sara Luengo-Sanchez Roberto Macho-Pedroso |
| author_sort | Carlos A. Luna |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:20:43Z |
| format | Article |
| id | doaj.art-f1636a9d6da147e7b50e7429864aa3f1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:20:43Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f1636a9d6da147e7b50e7429864aa3f12022-12-22T02:54:39ZengMDPI AGSensors1424-82202017-08-01178184510.3390/s17081845s17081845Headgear Accessories Classification Using an Overhead Depth SensorCarlos A. Luna0Javier Macias-Guarasa1Cristina Losada-Gutierrez2Marta Marron-Romera3Manuel Mazo4Sara Luengo-Sanchez5Roberto Macho-Pedroso6Department of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares, SpainDepartment of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares, SpainDepartment of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares, SpainDepartment of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares, SpainDepartment of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares, SpainDepartment of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares, SpainDepartment of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares, SpainIn 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.https://www.mdpi.com/1424-8220/17/8/1845headgear accessories classificationtime-of-flight sensorfeature extractionsemantic featuresdepth mapsoverhead camera |
| spellingShingle | Carlos A. Luna Javier Macias-Guarasa Cristina Losada-Gutierrez Marta Marron-Romera Manuel Mazo Sara Luengo-Sanchez Roberto Macho-Pedroso Headgear Accessories Classification Using an Overhead Depth Sensor Sensors headgear accessories classification time-of-flight sensor feature extraction semantic features depth maps overhead camera |
| title | Headgear Accessories Classification Using an Overhead Depth Sensor |
| title_full | Headgear Accessories Classification Using an Overhead Depth Sensor |
| title_fullStr | Headgear Accessories Classification Using an Overhead Depth Sensor |
| title_full_unstemmed | Headgear Accessories Classification Using an Overhead Depth Sensor |
| title_short | Headgear Accessories Classification Using an Overhead Depth Sensor |
| title_sort | headgear accessories classification using an overhead depth sensor |
| topic | headgear accessories classification time-of-flight sensor feature extraction semantic features depth maps overhead camera |
| url | https://www.mdpi.com/1424-8220/17/8/1845 |
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