A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of Panoramic Annular Lens for Motion Analysis Tasks: Surveillance and Smoke Detection
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
The Smoke Detection for Early Fire-Alarming System Base on Video Processing
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
Dynamic texture detection, segmentation and analysis
Proceedings of the 6th ACM international conference on Image and video retrieval
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video driven fire spread forecasting (f) using multi-modal LWIR and visual flame and smoke data
Pattern Recognition Letters
Digital Signal Processing
Multi-modal time-of-flight based fire detection
Multimedia Tools and Applications
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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.