The paper presents a new fast and robust technique of smoke detection in video surveillance images. The approach aims at detecting the spring or the presence of smoke by analyzing color and texture features of moving objects, segmented with background subtraction. The proposal embodies some novelties: first the temporal behavior of the smoke is modeled by a Mixture of Gaussians (MoG ) of the energy variation in the wavelet domain. The MoG takes into account the image energy variation due to either external luminance changes or the smoke propagation. It allows a distinction to energy variation due to the presence of real moving objects such as people and vehicles. Second, this textural analysis is enriched by a color analysis based on the blending function. Third, a Bayesian model is defined where the texture and color features, detected at block level, contributes to model the likelihood while a global evaluation of the entire image models the prior probability contribution. The resulting approach is very flexible and can be adopted in conjunction to a whichever video surveillance system based on dynamic background model. Several tests on tens of different contexts, both outdoor and indoor prove its robustness and precision. © 2008 Springer-Verlag Berlin Heidelberg.

Smoke detection in video surveillance: A MoG model in the wavelet domain / Calderara, Simone; Piccinini, Paolo; Cucchiara, Rita. - STAMPA. - 5008:(2008), pp. 119-128. [10.1007/978-3-540-79547-6_12]

Smoke detection in video surveillance: A MoG model in the wavelet domain

CALDERARA, Simone;PICCININI, PAOLO;CUCCHIARA, Rita
2008

Abstract

The paper presents a new fast and robust technique of smoke detection in video surveillance images. The approach aims at detecting the spring or the presence of smoke by analyzing color and texture features of moving objects, segmented with background subtraction. The proposal embodies some novelties: first the temporal behavior of the smoke is modeled by a Mixture of Gaussians (MoG ) of the energy variation in the wavelet domain. The MoG takes into account the image energy variation due to either external luminance changes or the smoke propagation. It allows a distinction to energy variation due to the presence of real moving objects such as people and vehicles. Second, this textural analysis is enriched by a color analysis based on the blending function. Third, a Bayesian model is defined where the texture and color features, detected at block level, contributes to model the likelihood while a global evaluation of the entire image models the prior probability contribution. The resulting approach is very flexible and can be adopted in conjunction to a whichever video surveillance system based on dynamic background model. Several tests on tens of different contexts, both outdoor and indoor prove its robustness and precision. © 2008 Springer-Verlag Berlin Heidelberg.
2008
5008
119
128
Calderara, Simone; Piccinini, Paolo; Cucchiara, Rita
Smoke detection in video surveillance: A MoG model in the wavelet domain / Calderara, Simone; Piccinini, Paolo; Cucchiara, Rita. - STAMPA. - 5008:(2008), pp. 119-128. [10.1007/978-3-540-79547-6_12]
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1112701
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 44
  • ???jsp.display-item.citation.isi??? 30
social impact