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http://cidesi.repositorioinstitucional.mx/jspui/handle/1024/415| Multi-sesor data fusion for people detection with a workspace monitoring system | |
| ALEJANDRO GHENNO BARAJAS | |
| Gengis Kanhg Toledo Ramírez | |
| Acceso Abierto | |
| Atribución-NoComercial | |
| ALGORITMO DETECCIÓN | |
| "This master thesis presents the application and testing of a workspace monitoring system for people detection in indoor environments. For this task, a probabilistic data fusion method is used to combine information from a set of two visual sensors, namely infrared and RGB cameras, and a LiDAR sensor. The extrinsic calibration of the cameras with a LiDAR sensor is performed to complement visual information with depth data. The resulting information is used in the fusion process to assign a confidence level to detections from each sensor taking into account two factors: the individual sensor’s confidence to detect people in point cloud, RGB or thermal images, and the proximity of the detected target to the monitoring system. People detection in RGB images, thermal images and point cloud data is achieved using machine learning techniques. The evaluation through experiments of the proposed workspace monitoring system results in a higher detection hit rate than using one of the sensors individually for people detection." | |
| 2020-01 | |
| Tesis de maestría | |
| Inglés | |
| ALE-MX | |
| Público en general | |
| INGENIERÍA DE CONTROL | |
| Aparece en las colecciones: | Desarrollo de sistemas de control, visión y algorítmos |
Cargar archivos:
| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| M-AGB-2020.pdf | 37.49 MB | Adobe PDF | Visualizar/Abrir |