Español
EnglishFast heuristic method to detect people in frontal depth images
| Title | Fast heuristic method to detect people in frontal depth images |
| Publication Type | Journal Article |
| Año de publicación | 2021 |
| Autores | Luna, CA, Losada-Gutiérrez, C, Fuentes-Jiménez, D, Mazo, M |
| Idioma de publicación | English |
| Journal | Expert Systems with Applications |
| Volumen | 168 |
| Páginas | 114483 |
| Fecha de publicación | 04/2021 |
| Palabras clave | 3D People detection, Depth camera, Feature extraction, Frontal Depth images, Head biometric classification |
| ISSN | 0957-4174 |
| URL | https://www.sciencedirect.com/science/article/pii/S0957417420311301 |
| DOI | https://doi.org/10.1016/j.eswa.2020.114483 |
| Abstract | This paper presents a new method for detecting people using only depth images captured by a camera in a frontal position. The approach is based on first detecting all the objects present in the scene and determining their average depth (distance to the camera). Next, for each object, a 3D Region of Interest (ROI) is processed around it in order to determine if the characteristics of the object correspond to the biometric characteristics of a human head. The results obtained using three public datasets captured by three depth sensors with different spatial resolutions and different operation principle (structured light, active stereo vision and Time of Flight) are presented. These results demonstrate that our method can run in realtime using a low-cost CPU platform with a high accuracy, being the processing times smaller than 1 ms per frame for a 512 × 424 image resolution with a precision of 99.26% and smaller than 4 ms per frame for a 1280 × 720 image resolution with a precision of 99.77%. |