Object detection is one of the most challenging issues for computer vision researchers. The analysis of the human visual attention mechanisms can help automatic inspection systems, in order to discard useless information and improving performances and efficiency. In this paper we proposed our attention based method to estimate firearms position in images of people holding firearms. Both top-down and bottom-up mechanisms are involved in our system. The bottom-up analysis is based on a state-of-the-art approach. The top-down analysis is based on the construction of a probabilistic model of the firearms position with respect to the people’s face position. This model has been created by analyzing information from of a public available database of movie frames representing actors holding firearms.
Ardizzone, E., Gallea, R., La Cascia, M., Mazzola, G. (2014). Combining Top-down and Bottom-up Visual Saliency for Firearms Localization. In SIGMAP 2014 - International Conference on Signal Processing and Multimedia Applications (pp. 25-32). SCITEPRESS – Science and Technology Publications.
Combining Top-down and Bottom-up Visual Saliency for Firearms Localization
ARDIZZONE, Edoardo;GALLEA, Roberto;LA CASCIA, Marco;MAZZOLA, Giuseppe
2014-01-01
Abstract
Object detection is one of the most challenging issues for computer vision researchers. The analysis of the human visual attention mechanisms can help automatic inspection systems, in order to discard useless information and improving performances and efficiency. In this paper we proposed our attention based method to estimate firearms position in images of people holding firearms. Both top-down and bottom-up mechanisms are involved in our system. The bottom-up analysis is based on a state-of-the-art approach. The top-down analysis is based on the construction of a probabilistic model of the firearms position with respect to the people’s face position. This model has been created by analyzing information from of a public available database of movie frames representing actors holding firearms.File | Dimensione | Formato | |
---|---|---|---|
SIGMAP_2014_33_CR.pdf
accesso aperto
Descrizione: articolo principale
Tipologia:
Versione Editoriale
Dimensione
2.48 MB
Formato
Adobe PDF
|
2.48 MB | Adobe PDF | Visualizza/Apri |
sigmap.pdf
accesso aperto
Descrizione: front cover + table of contents
Tipologia:
Versione Editoriale
Dimensione
4.77 MB
Formato
Adobe PDF
|
4.77 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.