Title :
Automatic Detection of Adverse Weather Conditions in Traffic Scenes
Author :
Lagorio, Andrea ; Grosso, Enrico ; Tistarelli, Massimo
Author_Institution :
Comput. Vision Lab. DEIR, Univ. of Sassari, Italy
Abstract :
Visual surveillance in outdoor environments requires the monitoring of both objects and events. The analysis is generally driven by the target application which, in turn, determines the set of relevant events and objects to be analyzed. In this paper we concentrate on the analysis of outdoor scenes, in particular for vehicle traffic control. In this scenario, the analysis of weather conditions is considered to signal particular and potentially dangerous situations like the presence of snow, fog, or heavy rain. The developed system uses a statistical framework based on the mixture of Gaussians to identify changes both in the spatial and temporal frequencies which characterize specific meteorological events. Several experiments performed on standard databases and real scenes demonstrate the applicability of the proposed approach.
Keywords :
Gaussian processes; image recognition; meteorology; object detection; road vehicles; statistical analysis; traffic control; video surveillance; Gaussian mixture; adverse weather condition automatic detection; outdoor scene analysis; statistical framework; traffic scenes; vehicle traffic control; visual surveillance; Frequency; Gaussian processes; Layout; Meteorology; Rain; Signal analysis; Snow; Surveillance; Traffic control; Vehicles; Gaussian Mixture Model; Visual surveillance; Weather recognition;
Conference_Titel :
Advanced Video and Signal Based Surveillance, 2008. AVSS '08. IEEE Fifth International Conference on
Conference_Location :
Santa Fe, NM
Print_ISBN :
978-0-7695-3341-4
Electronic_ISBN :
978-0-7695-3422-0
DOI :
10.1109/AVSS.2008.50