Combining degrees of normality analysis in intelligent surveillance systems

Advanced Surveillance Systems are able to automatically understand events and behaviors. These systems carry out an exhaustive analysis from multi-sensor information, according to multiple aspects or events of interest in order to classify situations as normal or abnormal. Thus, developing appropriate methods in order to combine the information from several criteria becomes critical to achieve a reliable interpretation in monitored environments. In this paper, we address the aggregation problem for multiple criteria in the domain of intelligent surveillance and analyze several alternatives to be put in practice. From these alternatives, we also propose a new aggregation method based on the Sugeno integral. All these methods have been evaluated within the context of OCULUS, an intelligent surveillance system that has been used to successfully monitor trajectories and speed of moving objects.