Fusión multicámarapara seguimiento de objetos basada en teoría evidencial
Using multiple cameras has proven to increase vision systems capabilities, mainly by extending visual field or complementing information from cameras to reduce uncertainty. While there are emerging approaches which take in consideration information such as where an object is standing or how long it...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2008 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/440 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/440 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Cámaras/Cameras info:eu-repo/classification/Rastreo/Tracking info:eu-repo/classification/Fusión/Fusion info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Sumario: | Using multiple cameras has proven to increase vision systems capabilities, mainly by extending visual field or complementing information from cameras to reduce uncertainty. While there are emerging approaches which take in consideration information such as where an object is standing or how long it has been on a particular zone, it is still no common to find work related to the use of multiple cameras to improve information useful for such surveillance systems. In this work a decision fusion level model is proposed to take advantage of several geographically distributed cameras, in order to reduce uncertainty derived from cameras’perspective. Previously defined zones are considered to track objects position, so the stage of the processing at which data integration takes place is useful for high level surveillance systems, such those focused on behavior recognition. In our model, individual decisions are taken by means of an axisprojection- based generalized basic belief assignment (gbba) function and finallyfused using Dezert-Smarandache (DSm) hybrid rule. It is also proposed a way to reduce and manage dynamically the frame of discernment to optimize computer resources. Results of model are presented, obtained from tests on animated simulations and real sequences, and compared to a bayesian fusion model. Experiments proved that the proposed model yields a good improvement in tracking accuracy at high level processing. |
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