Importance of detection for video surveillance applications
Though it is the first step of a real video surveillance application, detection has received less attention than tracking in research on video surveillance. We show, however, that the majority of errors in the tracking task are due to wrong detection. We show this by experimenting with a multi objec...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2008 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/13743 |
| Acceso en línea: | https://hdl.handle.net/2117/13743 https://dx.doi.org/10.1117/1.2965548 |
| Access Level: | acceso abierto |
| Palabra clave: | Video surveillance Visual tracking Target detection Vídeo vigilància Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| Sumario: | Though it is the first step of a real video surveillance application, detection has received less attention than tracking in research on video surveillance. We show, however, that the majority of errors in the tracking task are due to wrong detection. We show this by experimenting with a multi object tracking algorithm based on a Bayesian framework and a particle filter. This algorithm, which we have named iTrack, is specifically designed to work in practical applications by defining a statistical model of the object appearance to build a robust likelihood function. Likewise, we present an extension of a background subtraction algorithm to deal with active cameras. This algorithm is used in the detection task to initialize the tracker by means of a prior density. By defining appropriate performance metrics, the overall system is evaluated to elucidate the importance of detection for video surveillance applications. |
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